def btnLoadCondition_click(self):
msgBox = QMessageBox()
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
try:
Cond = InData[ui.txtCond.currentText()]
except:
msgBox.setText("Cannot find Condition ID!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
ui.txtClassName.clear()
ui.txtClassName.addItem("")
for condition in Cond:
ui.txtClassName.addItem(condition[0][0])
def btnReport_click(self):
msgBox = QMessageBox()
Vars = str(ui.txtVars.text()).replace(",", " ").split()
if not len(Vars):
msgBox.setText("Please enter variables!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
if ui.lvFile.count() <= 0:
msgBox.setText("Please enter files!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
if ui.cbClear.isChecked():
ui.txtReport.clear()
Len = ui.txtChar.value()
str1 = ""
for var in Vars:
str1 = str1 + "\t" + var if str != "" else var
ui.txtReport.append(str1 + "\n")
for itemIndx in range(ui.lvFile.count()):
filename = ui.lvFile.item(itemIndx).text()
print("Reading " + filename)
data = mainIO_load(filename)
dat1 = ""
if ui.rbAddress.isChecked():
str1 = filename
elif ui.rbNone.isChecked():
str1 = ""
elif ui.rbIndex.isChecked():
str1 = str(itemIndx)
else:
str1 = os.path.split(os.path.abspath(filename))[1]
if not ui.rbNone.isChecked():
if len(str1) < Len:
for _ in range(Len - len(str1)):
str1 = str1 + ' '
else:
str1 = str1[0:Len]
for var in Vars:
try:
dat1 = str(data[var])
except:
dat1 = 'None'
str1 = str1 + "\t" + dat1 if str1 != "" else dat1
ui.txtReport.append(str1 + "\n")
def btnAnalysisFile_click(self):
filename = LoadFile("Load data file ...",['Data files (*.ezx *.mat *.ezdata)'],'ezx',\
os.path.dirname(ui.txtAnFile.text()))
# Load File
dat = None
try:
dat = mainIO_load(filename)
except:
print("Cannot load Analysis file!")
return
# Load Result
results = None
try:
results = dat["results"]
except:
print(
"Cannot find results field --- i.e., 2D matrix including coordinates and accuracy"
)
return
# Clear Views
ui.vwSele.clear()
ui.vwSele.setColumnCount(2)
ui.vwSele.setRowCount(0)
ui.vwSele.setColumnWidth(0, 120)
ui.vwSele.setColumnWidth(1, 100)
ui.vwSele.setHorizontalHeaderLabels(['Coordinate', 'Accuracy'])
ui.vwSele.setSortingEnabled(True)
ui.vwSele.setSelectionMode(QtWidgets.QAbstractItemView.MultiSelection)
ui.vwSele.setSelectionBehavior(QAbstractItemView.SelectRows)
ui.vwAvai.clear()
ui.vwAvai.setColumnCount(2)
ui.vwSele.setRowCount(0)
ui.vwAvai.setColumnWidth(0, 120)
ui.vwAvai.setColumnWidth(1, 100)
ui.vwAvai.setHorizontalHeaderLabels(['Coordinate', 'Accuracy'])
ui.vwAvai.setSortingEnabled(True)
ui.vwAvai.setSelectionMode(QtWidgets.QAbstractItemView.MultiSelection)
ui.vwAvai.setSelectionBehavior(QAbstractItemView.SelectRows)
# Draw
ui.vwAvai.setRowCount(np.shape(results)[0])
ui.vwAvai.setRowCount(np.shape(results)[0])
for rowId, (coo, acc) in enumerate(results):
try:
item = QTableWidgetItem('{:5};{:5};{:5}'.format(
coo[0][0], coo[0][1], coo[0][2]))
item.setFlags(QtCore.Qt.ItemIsSelectable
| QtCore.Qt.ItemIsEnabled)
ui.vwAvai.setItem(rowId, 0, item)
item = QTableWidgetItem('{:8f}'.format(acc[0][0] * 100))
item.setFlags(QtCore.Qt.ItemIsSelectable
| QtCore.Qt.ItemIsEnabled)
ui.vwAvai.setItem(rowId, 1, item)
except:
print(f'Item: {rowId} cannot add to the list --- format error')
print(f"We have added {np.shape(results)[0]} items")
ui.txtAnFile.setText(filename)
ui.tabWidget.setCurrentIndex(1)
def btnRedraw_click(self):
msgBox = QMessageBox()
ofile = LoadFile("Save result file ...", ['Result files (*.ezx *.mat)'], 'ezx',\
os.path.dirname(ui.txtOutFile.text()))
FontSize = ui.txtFontSize.value()
if len(ofile):
try:
Res = mainIO_load(ofile)
except:
print("Cannot load result file!")
msgBox.setText("Cannot load result file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Load Variables
try:
labels = list(Res["labels"])
Z = Res["Linkage"]
if ui.cbCorr.isChecked():
Corr = Res["Correlation"]
if ui.cbCov.isChecked():
Cov = Res["Covariance"]
except Exception as e:
print(str(e))
msgBox.setText(str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
drawrsa = DrawRSA()
if ui.cbCorr.isChecked():
drawrsa.ShowFigure(Corr, labels, ui.txtTitleCorr.text(),
FontSize, ui.txtXRotation.value(),
ui.txtYRotation.value())
if ui.cbCov.isChecked():
drawrsa.ShowFigure(Cov, labels, ui.txtTitleCov.text(),
FontSize, ui.txtXRotation.value(),
ui.txtYRotation.value())
drawrsa.ShowDend(Z, labels, ui.txtTitleDen.text(), FontSize,
ui.txtXRotation.value())
def btnInData_click(self):
filename = LoadFile("Load data file ...",['Data files (*.ezx *.mat *.ezdata)'],'ezx',\
os.path.dirname(ui.txtInData.text()))
if len(filename):
if os.path.isfile(filename):
try:
print("Data is loading ...")
data = mainIO_load(filename)
Keys = data.keys()
# Test Data
ui.txtITeData.clear()
HasDefualt = False
for key in Keys:
ui.txtITeData.addItem(key)
if key == "test_data":
HasDefualt = True
if HasDefualt:
ui.txtITeData.setCurrentText("test_data")
# Test Label
ui.txtITeLabel.clear()
ui.txtClass.clear()
HasDefualt = False
for key in Keys:
ui.txtITeLabel.addItem(key)
if key == "test_label":
HasDefualt = True
if HasDefualt:
ui.txtITeLabel.setCurrentText("test_label")
# set number of features
Labels = data[ui.txtITeLabel.currentText()]
Labels = np.unique(Labels)
lblString = None
for lbl in Labels:
lblString = str(
lbl
) if lblString is None else lblString + ", " + str(
lbl)
ui.txtClass.setText(lblString.strip())
ui.txtInData.setText(filename)
print("Data is ready.")
except:
print("Cannot load data file!")
return
def OpenFile(self, ifile):
global data
global root
if len(ifile):
if os.path.isfile(ifile):
try:
data = mainIO_load(ifile)
assert data is not None
except:
print("Cannot load file!")
return
root = queue.Queue()
frmDataEditor.DrawData(self)
ui.txtInFile.setText(ifile)
print(ifile + " is loaded!")
ui.btnSave.setEnabled(False)
def btnInFile_click(self):
filename = LoadFile("Load data file ...",['Data files (*.ezx *.mat *.ezdata)'],'ezx',\
os.path.dirname(ui.txtInFile.text()))
if len(filename):
if os.path.isfile(filename):
try:
data = mainIO_load(filename)
Keys = data.keys()
# Train Data
ui.txtData.clear()
HasDefualt = False
for key in Keys:
ui.txtData.addItem(key)
if key == "data":
HasDefualt = True
if HasDefualt:
ui.txtData.setCurrentText("data")
# Train Label
ui.txtLabel.clear()
ui.txtClass.clear()
HasDefualt = False
for key in Keys:
ui.txtLabel.addItem(key)
if key == "label":
HasDefualt = True
if HasDefualt:
ui.txtLabel.setCurrentText("label")
# set number of features
Labels = data[ui.txtLabel.currentText()]
Labels = np.unique(Labels)
ui.txtClass.clear()
for lbl in Labels:
ui.txtClass.append(str(lbl))
ui.txtInFile.setText(filename)
except Exception as e:
print(e)
print("Cannot load data file!")
return
else:
print("File not found!")
def btnRedraw_click(self):
msgBox = QMessageBox()
try:
EdgeThreshold = np.float(ui.txtEdgeThre.text())
assert EdgeThreshold <= 1
assert EdgeThreshold >= 0
except:
msgBox.setText("Edge threshold is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
ofile = LoadFile("Load result file ...", ['Result files (*.ezx *.mat)'], 'ezx',\
os.path.dirname(ui.txtOutFile.text()))
if len(ofile):
Out = {}
try:
Out = mainIO_load(ofile)
except:
print("Cannot load result file!")
msgBox.setText("Cannot load result file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
Net = Out["Networks"]
ThrNet = Out["ThresholdNetworks"]
ACoord = Out["Atlas_parcellation"]
for nnIndex, (nn, tnn) in enumerate(zip(Net, ThrNet)):
try:
Cond = Out["condition"]
Title = f"Label: {reshape_condition_cell(Cond[nnIndex][1])}"
except:
Title = f"Label: {nnIndex}"
PlotConnectome(tnn, ACoord, Title, EdgeThreshold)
fig = plt.figure()
plt.imshow(np.transpose(nn))
plt.title(Title)
plt.show()
def btnMerge_click(self):
global data
global root
if len(ui.txtInFile.text()):
ifile = LoadFile("Open data files ...",\
['Data files (*.ezx *.mat *.ezdata *.ezmat, *.model)', "easyX files (*.ezx)", 'MatLab files (*.mat)','EasyData files (*.ezdata)', \
'EasyMat (*.ezmat)', 'All files (*.*)'],'mat')
if len(ifile):
if os.path.isfile(ifile):
try:
data2 = mainIO_load(ifile)
except:
print("Cannot load file!")
return
for key in data2.keys():
if key != "__header__" and key != "__version__" and key != "__globals__":
data[key] = data2[key]
root = queue.Queue()
frmDataEditor.DrawData(self)
ui.btnSave.setEnabled(True)
def btnInFile_click(self):
filename = LoadFile("Load data file ...",['Data files (*.ezx *.mat *.ezdata)'],'ezx',\
os.path.dirname(ui.txtInFile.text()))
if len(filename):
if os.path.isfile(filename):
try:
data = mainIO_load(filename)
Keys = data.keys()
# Train Data
ui.txtITrData.clear()
HasDefualt = False
for key in Keys:
ui.txtITrData.addItem(key)
if key == "train_data":
HasDefualt = True
if HasDefualt:
ui.txtITrData.setCurrentText("train_data")
# Test Data
ui.txtITeData.clear()
HasDefualt = False
for key in Keys:
ui.txtITeData.addItem(key)
if key == "test_data":
HasDefualt = True
if HasDefualt:
ui.txtITeData.setCurrentText("test_data")
# Train Label
ui.txtITrLabel.clear()
HasDefualt = False
for key in Keys:
ui.txtITrLabel.addItem(key)
if key == "train_label":
HasDefualt = True
if HasDefualt:
ui.txtITrLabel.setCurrentText("train_label")
# Test Label
ui.txtITeLabel.clear()
HasDefualt = False
for key in Keys:
ui.txtITeLabel.addItem(key)
if key == "test_label":
HasDefualt = True
if HasDefualt:
ui.txtITeLabel.setCurrentText("test_label")
# Train mLabel
ui.txtITrmLabel.clear()
HasDefualt = False
for key in Keys:
ui.txtITrmLabel.addItem(key)
if key == "train_mlabel":
HasDefualt = True
if HasDefualt:
ui.txtITrmLabel.setCurrentText("train_mlabel")
ui.cbmLabel.setChecked(HasDefualt)
# Test mLabel
ui.txtITemLabel.clear()
HasDefualt = False
for key in Keys:
ui.txtITemLabel.addItem(key)
if key == "test_mlabel":
HasDefualt = True
if HasDefualt:
ui.txtITemLabel.setCurrentText("test_mlabel")
if ui.cbmLabel.isChecked():
ui.cbmLabel.setChecked(HasDefualt)
# Coordinate
ui.txtCol.clear()
HasDefualt = False
for key in Keys:
ui.txtCol.addItem(key)
if key == "coordinate":
HasDefualt = True
if HasDefualt:
ui.txtCol.setCurrentText("coordinate")
ui.cbCol.setChecked(HasDefualt)
# Train Design
ui.txtITrDM.clear()
HasDefualt = False
for key in Keys:
ui.txtITrDM.addItem(key)
if key == "train_design":
HasDefualt = True
if HasDefualt:
ui.txtITrDM.setCurrentText("train_design")
ui.cbDM.setChecked(HasDefualt)
# Test Design
ui.txtITeDM.clear()
HasDefualt = False
for key in Keys:
ui.txtITeDM.addItem(key)
if key == "test_design":
HasDefualt = True
if HasDefualt:
ui.txtITeDM.setCurrentText("test_design")
if ui.cbDM.isChecked():
ui.cbDM.setChecked(HasDefualt)
# Train Subject
ui.txtITrSubject.clear()
HasDefualt = False
for key in Keys:
ui.txtITrSubject.addItem(key)
if key == "train_subject":
HasDefualt = True
if HasDefualt:
ui.txtITrSubject.setCurrentText("train_subject")
# Test Subject
ui.txtITeSubject.clear()
HasDefualt = False
for key in Keys:
ui.txtITeSubject.addItem(key)
if key == "test_subject":
HasDefualt = True
if HasDefualt:
ui.txtITeSubject.setCurrentText("test_subject")
# Train Task
ui.txtITrTask.clear()
HasDefualt = False
for key in Keys:
ui.txtITrTask.addItem(key)
if key == "train_task":
HasDefualt = True
if HasDefualt:
ui.txtITrTask.setCurrentText("train_task")
ui.cbTask.setChecked(HasDefualt)
# Test Task
ui.txtITeTask.clear()
HasDefualt = False
for key in Keys:
ui.txtITeTask.addItem(key)
if key == "test_task":
HasDefualt = True
if HasDefualt:
ui.txtITeTask.setCurrentText("test_task")
if ui.cbTask.isChecked():
ui.cbTask.setChecked(HasDefualt)
# Train Run
ui.txtITrRun.clear()
HasDefualt = False
for key in Keys:
ui.txtITrRun.addItem(key)
if key == "train_run":
HasDefualt = True
if HasDefualt:
ui.txtITrRun.setCurrentText("train_run")
ui.cbRun.setChecked(HasDefualt)
# Test Run
ui.txtITeRun.clear()
HasDefualt = False
for key in Keys:
ui.txtITeRun.addItem(key)
if key == "test_run":
HasDefualt = True
if HasDefualt:
ui.txtITeRun.setCurrentText("test_run")
if ui.cbRun.isChecked():
ui.cbRun.setChecked(HasDefualt)
# Train Counter
ui.txtITrCounter.clear()
HasDefualt = False
for key in Keys:
ui.txtITrCounter.addItem(key)
if key == "train_counter":
HasDefualt = True
if HasDefualt:
ui.txtITrCounter.setCurrentText("train_counter")
ui.cbCounter.setChecked(HasDefualt)
# Test Counter
ui.txtITeCounter.clear()
HasDefualt = False
for key in Keys:
ui.txtITeCounter.addItem(key)
if key == "test_counter":
HasDefualt = True
if HasDefualt:
ui.txtITeCounter.setCurrentText("test_counter")
if ui.cbCounter.isChecked():
ui.cbCounter.setChecked(HasDefualt)
# Condition
ui.txtCond.clear()
HasDefualt = False
for key in Keys:
ui.txtCond.addItem(key)
if key == "condition":
HasDefualt = True
if HasDefualt:
ui.txtCond.setCurrentText("condition")
ui.cbCond.setChecked(HasDefualt)
# Train NScan
ui.txtITrScan.clear()
HasDefualt = False
for key in Keys:
ui.txtITrScan.addItem(key)
if key == "train_nscan":
HasDefualt = True
if HasDefualt:
ui.txtITrScan.setCurrentText("train_nscan")
ui.cbNScan.setChecked(HasDefualt)
# Test NScan
ui.txtITeScan.clear()
HasDefualt = False
for key in Keys:
ui.txtITeScan.addItem(key)
if key == "test_nscan":
HasDefualt = True
if HasDefualt:
ui.txtITeScan.setCurrentText("test_nscan")
if ui.cbNScan.isChecked():
ui.cbNScan.setChecked(HasDefualt)
# FoldID
ui.txtFoldID.clear()
HasDefualt = False
for key in Keys:
ui.txtFoldID.addItem(key)
if key == "FoldID":
HasDefualt = True
if HasDefualt:
ui.txtFoldID.setCurrentText("FoldID")
ui.cbFoldID.setChecked(HasDefualt)
# FoldInfo
ui.txtFoldInfo.clear()
HasDefualt = False
for key in Keys:
ui.txtFoldInfo.addItem(key)
if key == "FoldInfo":
HasDefualt = True
if HasDefualt:
ui.txtFoldInfo.setCurrentText("FoldInfo")
ui.cbFoldInfo.setChecked(HasDefualt)
# set number of features
XShape = np.shape(data[ui.txtITrData.currentText()])
ui.txtNumFea.setMaximum(0)
ui.txtNumFea.setMaximum(XShape[1])
ui.txtNumFea.setValue(0)
ui.lblFeaNum.setText("1 ... " + str(XShape[1]) +
", 0 = auto")
if ui.cbFoldID.isChecked():
ui.lbFoldID.setText(
"ID=" +
str(data[ui.txtFoldID.currentText()][0][0]))
ui.txtInFile.setText(filename)
except Exception as e:
print(e)
print("Cannot load data file!")
return
else:
print("File not found!")
def btnConvert_click(self):
msgBox = QMessageBox()
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
if FoldTo < FoldFrom:
print("Please check fold parameters!")
return
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace(
"[", "").replace("]", "").split()
Filter = np.int32(Filter)
except:
print("Class filter is wrong!")
msgBox.setText("Class filter is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
Fold = list()
accuracy = list()
precision = list()
average_precision = list()
f1score = list()
recall = list()
accuracyTr = list()
precisionTr = list()
average_precisionTr = list()
f1scoreTr = list()
recallTr = list()
InFileList = list()
OutData = dict()
OutData["ModelAnalysis"] = "Gaussian Naive Bayes"
for fold in range(FoldFrom, FoldTo + 1):
# OutModel
OutModel = ui.txtOutModel.text()
if not len(OutModel):
OutModel = None
else:
OutModel = OutModel.replace("$FOLD$", str(fold))
# InFile
InFile = ui.txtInFile.text()
InFile = InFile.replace("$FOLD$", str(fold))
InFileList.append(InFile)
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TrX = InData[ui.txtITrData.currentText()]
TeX = InData[ui.txtITeData.currentText()]
TrL = InData[ui.txtITrLabel.currentText()][0]
TeL = InData[ui.txtITeLabel.currentText()][0]
# Ref Filter Train
if ui.cbFilterTrID.isChecked():
try:
# Create content structure for training
TrFilterContent = ui.txtFilterTrContent.text()
TrFilterContent = TrFilterContent.replace(" ", "")
if not len(TrFilterContent):
print("Reference filter for training is wrong!")
msgBox.setText(
"Reference filter for training is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
else:
TrFilterContent = TrFilterContent.replace(
"\'",
" ").replace(",",
" ").replace("[",
"").replace("]",
"").split()
TrFilterContent = np.int32(TrFilterContent)
# Check Filter ID for training
if not len(ui.txtFilterTrID.currentText()):
msgBox.setText(
"Please enter variable name for training filter!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TrF = InData[ui.txtFilterTrID.currentText()][0]
if np.shape(TrX)[0] != np.shape(TrF)[0] or np.shape(
TrL)[0] != np.shape(TrF)[0]:
print(
"Shape of reference for training must be the same as data and label"
)
msgBox.setText(
"Shape of reference for training must be the same as data and label"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
except:
print("Reference filter for training is wrong!")
msgBox.setText("Reference filter for training is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Remove training set
try:
print(
"Removing training set based on reference. Data shape: ",
np.shape(TrX), "Label shape: ", np.shape(TrL))
for content in TrFilterContent:
contIndex = np.where(TrF == content)[0]
TrL = np.delete(TrL, contIndex, axis=0)
TrX = np.delete(TrX, contIndex, axis=0)
TrF = np.delete(TrF, contIndex, axis=0)
print(" Content", content,
"is removed from training set. Data shape: ",
np.shape(TrX), "Label shape: ", np.shape(TrL))
except Exception as e:
print("Cannot filter the training set based on Reference")
print(str(e))
msgBox.setText(
"Cannot filter the training set based on Reference")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Ref Filter Test
if ui.cbFilterTeID.isChecked():
try:
# Create content structure for testing
TeFilterContent = ui.txtFilterTeContent.text()
TeFilterContent = TeFilterContent.replace(" ", "")
if not len(TeFilterContent):
print("Reference filter for testing is wrong!")
msgBox.setText(
"Reference filter for testing is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
else:
TeFilterContent = TeFilterContent.replace(
"\'",
" ").replace(",",
" ").replace("[",
"").replace("]",
"").split()
TeFilterContent = np.int32(TeFilterContent)
# Check Filter ID for testing
if not len(ui.txtFilterTeID.currentText()):
msgBox.setText(
"Please enter variable name for testing filter!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TeF = InData[ui.txtFilterTeID.currentText()][0]
if np.shape(TeX)[0] != np.shape(TeF)[0] or np.shape(
TeL)[0] != np.shape(TeF)[0]:
print(
"Shape of reference for testing must be the same as data and label"
)
msgBox.setText(
"Shape of reference for testing must be the same as data and label"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
except:
print("Reference filter for testing is wrong!")
msgBox.setText("Reference filter for testing is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Remove testing set
try:
print(
"Removing testing set based on reference. Data shape: ",
np.shape(TeX), "Label shape: ", np.shape(TeL))
for content in TeFilterContent:
contIndex = np.where(TeF == content)[0]
TeL = np.delete(TeL, contIndex, axis=0)
TeX = np.delete(TeX, contIndex, axis=0)
TeF = np.delete(TeF, contIndex, axis=0)
print(" Content", content,
"is removed from testing set. Data shape: ",
np.shape(TeX), "Label shape: ", np.shape(TeL))
except Exception as e:
print("Cannot filter the testing set based on Reference")
print(str(e))
msgBox.setText(
"Cannot filter the testing set based on Reference")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
try:
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(TrL == fil)[0]
TrL = np.delete(TrL, labelIndx, axis=0)
TrX = np.delete(TrX, labelIndx, axis=0)
# Remove Testing Set
labelIndx = np.where(TeL == fil)[0]
TeL = np.delete(TeL, labelIndx, axis=0)
TeX = np.delete(TeX, labelIndx, axis=0)
print("Class ID = " + str(fil) +
" is removed from data.")
if ui.cbScale.isChecked():
TrX = preprocessing.scale(TrX)
TeX = preprocessing.scale(TeX)
print(
"Whole of data is scaled Train~N(0,1) and Test~N(0,1)."
)
except:
print("Cannot load data or label")
return
# FoldID
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
currFID = InData[ui.txtFoldID.currentText()][0][0]
Fold.append(currFID)
except:
print("Cannot load Fold ID!")
return
try:
clf = GaussianNB()
print("FoldID = " + str(currFID) + " is training ...")
clf.fit(TrX, TrL)
if OutModel is not None:
joblib.dump(clf, OutModel)
print("FoldID = " + str(currFID) + " Model is saved: " +
OutModel)
print("FoldID = " + str(currFID) + " is testing ...")
PeL = clf.predict(TeX)
PrL = clf.predict(TrX)
OutData["fold" + str(currFID) +
"_confusion_matrix"] = confusion_matrix(
TeL, PeL, np.unique(TeL))
OutData["fold" + str(currFID) +
"_classification_report"] = classification_report(
TeL, PeL)
print(OutData["fold" + str(currFID) +
"_classification_report"])
except Exception as e:
print(e)
msgBox = QMessageBox()
msgBox.setText(str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
if ui.cbAverage.isChecked():
acc = accuracy_score(TeL, PeL)
accTr = accuracy_score(TrL, PrL)
accuracy.append(acc)
accuracyTr.append(accTr)
print(
"FoldID = {:d}, Average Train {:5.2f} Test {:5.2f}"
.format(currFID, accTr * 100, acc * 100))
if ui.cbPrecision.isChecked():
pre = precision_score(TeL,
PeL,
average=ui.cbPrecisionAvg.currentData())
preTr = precision_score(
TrL, PrL, average=ui.cbPrecisionAvg.currentData())
precision.append(pre)
precisionTr.append(preTr)
print(
"FoldID = {:d}, Precision Train {:5.2f} Test {:5.2f}"
.format(currFID, preTr * 100, pre * 100))
if ui.cbAPrecision.isChecked():
prA = average_precision_score(
TeL, PeL, average=ui.cbAPrecisionAvg.currentData())
prATr = average_precision_score(
TrL, PrL, average=ui.cbAPrecisionAvg.currentData())
average_precision.append(prA)
average_precisionTr.append(prATr)
print(
"FoldID = {:d}, Average Precision: Train {:5.2f} Test {:5.2f}"
.format(currFID, prATr * 100, prA * 100))
if ui.cbRecall.isChecked():
rec = recall_score(TeL,
PeL,
average=ui.cbRecallAvg.currentData())
recTr = recall_score(TrL,
PrL,
average=ui.cbRecallAvg.currentData())
recall.append(rec)
recallTr.append(recTr)
print(
"FoldID = {:d}, Recall: Train {:5.2f} Test {:5.2f}"
.format(currFID, recTr * 100, rec * 100))
if ui.cbF1.isChecked():
f1 = f1_score(TeL, PeL, average=ui.cbF1Avg.currentData())
f1Tr = f1_score(TrL, PrL, average=ui.cbF1Avg.currentData())
f1score.append(f1)
f1scoreTr.append(f1Tr)
print(
"FoldID = {:d}, F1: Train {:5.2f} Test {:5.2f}"
.format(currFID, f1Tr * 100, f1 * 100))
print("FoldID = " + str(currFID) + " is analyzed!")
if ui.cbAverage.isChecked():
OutData["FoldAccuracy"] = accuracy
MeanAcc = np.mean(accuracy)
OutData["MeanTestAccuracy"] = MeanAcc
STDAcc = np.std(accuracy)
OutData["StdTestAccuracy"] = STDAcc
MeanAccTr = np.mean(accuracyTr)
OutData["MeanTrainAccuracy"] = MeanAccTr
STDAccTr = np.std(accuracyTr)
OutData["StdTrainAccuracy"] = STDAccTr
print(
"Accuracy: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanAccTr * 100, STDAccTr, MeanAcc * 100, STDAcc))
if ui.cbPrecision.isChecked():
OutData["ModePrecision"] = ui.cbPrecisionAvg.currentText()
OutData["FoldPrecision"] = precision
MeanPre = np.mean(precision)
OutData["MeanTrainPrecision"] = MeanPre
STDPre = np.std(precision)
OutData["StdTrainPrecision"] = STDPre
MeanPreTr = np.mean(precisionTr)
OutData["MeanTestPrecision"] = MeanPreTr
STDPreTr = np.std(precisionTr)
OutData["StdTestPrecision"] = STDPreTr
print(
"Precision: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanPreTr * 100, STDPreTr, MeanPre * 100, STDPre))
if ui.cbAPrecision.isChecked():
OutData["ModeAveragePrecision"] = ui.cbAPrecisionAvg.currentText()
OutData["FoldAveragePrecision"] = average_precision
MeanAPre = np.mean(average_precision)
OutData["MeanTrainAveragePrecision"] = MeanAPre
STDAPre = np.std(average_precision)
OutData["StdTestAveragePrecision"] = STDAPre
MeanAPreTr = np.mean(average_precisionTr)
OutData["MeanTrainAveragePrecision"] = MeanAPreTr
STDAPreTr = np.std(average_precisionTr)
OutData["StdTrainAveragePrecision"] = STDAPreTr
print(
"AveragePrecision: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanAPreTr * 100, STDAPreTr, MeanAPre * 100, STDAPre))
if ui.cbRecall.isChecked():
OutData["ModeRecall"] = ui.cbRecallAvg.currentText()
OutData["FoldRecall"] = recall
MeanRec = np.mean(recall)
OutData["MeanTestRecall"] = MeanRec
STDRec = np.std(recall)
OutData["StdTestRecall"] = STDRec
MeanRecTr = np.mean(recallTr)
OutData["MeanTrainRecall"] = MeanRecTr
STDRecTr = np.std(recallTr)
OutData["StdTrainRecall"] = STDRecTr
print(
"Recall: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanRecTr * 100, STDRecTr, MeanRec * 100, STDRec))
if ui.cbF1.isChecked():
OutData["ModeF1"] = ui.cbF1Avg.currentText()
OutData["FoldF1"] = f1score
MeanF1 = np.mean(f1score)
OutData["MeanTestF1"] = MeanF1
STDF1 = np.std(f1score)
OutData["StdTestF1"] = STDF1
MeanF1Tr = np.mean(f1scoreTr)
OutData["MeanTrainF1"] = MeanF1Tr
STDF1Tr = np.std(f1scoreTr)
OutData["StdTrainF1"] = STDF1Tr
print(
"F1: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanF1Tr * 100, STDF1Tr, MeanF1 * 100, STDF1))
OutData["InputFiles"] = InFileList
print("Saving ...")
mainIO_save(OutData, OutFile)
print("DONE.")
msgBox.setText("Gaussian Naive Bayes is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
totalTime = 0
Alg = ui.cbAlg.currentText()
# Tol
try:
Tol = np.float(ui.txtTole.text())
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# MaxIte
try:
MaxIter = np.int32(ui.txtMaxIter.text())
except:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if MaxIter < 1:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
if FoldTo < FoldFrom:
print("Please check fold parameters!")
return
for fold_all in range(FoldFrom, FoldTo+1):
tic = time.time()
# OutFile
OutFile = ui.txtOutFile.text()
OutFile = OutFile.replace("$FOLD$", str(fold_all))
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
InFile = InFile.replace("$FOLD$", str(fold_all))
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrData.text()):
msgBox.setText("Please enter Output Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeData.text()):
msgBox.setText("Please enter Output Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
XTr = InData[ui.txtITrData.currentText()]
XTe = InData[ui.txtITeData.currentText()]
if ui.cbScale.isChecked():
XTr = preprocessing.scale(XTr)
XTe = preprocessing.scale(XTe)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
try:
NumFea = np.int32(ui.txtNumFea.text())
except:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea < 1:
msgBox.setText("Number of features must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(XTr)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(XTe)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrLabel.text()):
msgBox.setText("Please enter Train Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeLabel.text()):
msgBox.setText("Please enter Test Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrLabel.text()] = reshape_1Dvector(InData[ui.txtITrLabel.currentText()])
OutData[ui.txtOTeLabel.text()] = reshape_1Dvector(InData[ui.txtITeLabel.currentText()])
except:
print("Cannot load labels!")
# Subject
if not len(ui.txtITrSubject.currentText()):
msgBox.setText("Please enter Train Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeSubject.currentText()):
msgBox.setText("Please enter Test Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrSubject.text()):
msgBox.setText("Please enter Train Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeSubject.text()):
msgBox.setText("Please enter Test Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrSubject = InData[ui.txtITrSubject.currentText()]
OutData[ui.txtOTrSubject.text()] = reshape_1Dvector(TrSubject)
TeSubject = InData[ui.txtITeSubject.currentText()]
OutData[ui.txtOTeSubject.text()] = reshape_1Dvector(TeSubject)
except:
print("Cannot load Subject IDs")
return
# Task
if ui.cbTask.isChecked():
if not len(ui.txtITrTask.currentText()):
msgBox.setText("Please enter Input Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeTask.currentText()):
msgBox.setText("Please enter Input Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrTask.text()):
msgBox.setText("Please enter Output Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeTask.text()):
msgBox.setText("Please enter Output Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrTask = np.asarray(InData[ui.txtITrTask.currentText()])
OutData[ui.txtOTrTask.text()] = reshape_1Dvector(TrTask)
TeTask = np.asarray(InData[ui.txtITeTask.currentText()])
OutData[ui.txtOTeTask.text()] = reshape_1Dvector(TeTask)
TrTaskIndex = TrTask.copy()
for tasindx, tas in enumerate(np.unique(TrTask)):
TrTaskIndex[TrTask == tas] = tasindx + 1
TeTaskIndex = TeTask.copy()
for tasindx, tas in enumerate(np.unique(TeTask)):
TeTaskIndex[TeTask == tas] = tasindx + 1
except:
print("Cannot load Tasks!")
return
# Run
if ui.cbRun.isChecked():
if not len(ui.txtITrRun.currentText()):
msgBox.setText("Please enter Train Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeRun.currentText()):
msgBox.setText("Please enter Test Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrRun.text()):
msgBox.setText("Please enter Train Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeRun.text()):
msgBox.setText("Please enter Test Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrRun = InData[ui.txtITrRun.currentText()]
OutData[ui.txtOTrRun.text()] = reshape_1Dvector(TrRun)
TeRun = InData[ui.txtITeRun.currentText()]
OutData[ui.txtOTeRun.text()] = reshape_1Dvector(TeRun)
except:
print("Cannot load Runs!")
return
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtITrCounter.currentText()):
msgBox.setText("Please enter Train Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeCounter.currentText()):
msgBox.setText("Please enter Test Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrCounter.text()):
msgBox.setText("Please enter Train Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeCounter.text()):
msgBox.setText("Please enter Test Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrCounter = InData[ui.txtITrCounter.currentText()]
OutData[ui.txtOTrCounter.text()] = reshape_1Dvector(TrCounter)
TeCounter = InData[ui.txtITeCounter.currentText()]
OutData[ui.txtOTeCounter.text()] = reshape_1Dvector(TeCounter)
except:
print("Cannot load Counters!")
return
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtITrmLabel.currentText()):
msgBox.setText("Please enter Train Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITemLabel.currentText()):
msgBox.setText("Please enter Test Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrmLabel.text()):
msgBox.setText("Please enter Train Output Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTemLabel.text()):
msgBox.setText("Please enter Test Output Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrmLabel.text()] = InData[ui.txtITrmLabel.currentText()]
OutData[ui.txtOTemLabel.text()] = InData[ui.txtITemLabel.currentText()]
except:
print("Cannot load matrix lables!")
return
# Design
if ui.cbDM.isChecked():
if not len(ui.txtITrDM.currentText()):
msgBox.setText("Please enter Train Input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeDM.currentText()):
msgBox.setText("Please enter Test Input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrDM.text()):
msgBox.setText("Please enter Train Output Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeDM.text()):
msgBox.setText("Please enter Test Output Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrDM.text()] = InData[ui.txtITrDM.currentText()]
OutData[ui.txtOTeDM.text()] = InData[ui.txtITeDM.currentText()]
except:
print("Cannot load design matrices!")
return
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCol.text()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCol.text()] = InData[ui.txtCol.currentText()]
except:
print("Cannot load coordinator!")
return
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCond.text()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCond.text()] = InData[ui.txtCond.currentText()]
except:
print("Cannot load conditions!")
return
# FoldID
if ui.cbFoldID.isChecked():
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldID.text()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldID.text()] = reshape_1Dvector(InData[ui.txtFoldID.currentText()])
except:
print("Cannot load Fold ID!")
return
# FoldInfo
if ui.cbFoldInfo.isChecked():
if not len(ui.txtFoldInfo.currentText()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldInfo.text()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldInfo.text()] = InData[ui.txtFoldInfo.currentText()]
except:
print("Cannot load Fold Info!")
return
pass
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtITrScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name for Input Train!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name for Input Test!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrScan.text()):
msgBox.setText("Please enter Number of Scan variable name for Output Train!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeScan.text()):
msgBox.setText("Please enter Number of Scan variable name for Output Test!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrScan.text()] = reshape_1Dvector(InData[ui.txtITrScan.currentText()])
OutData[ui.txtOTeScan.text()] = reshape_1Dvector(InData[ui.txtITeScan.currentText()])
except:
print("Cannot load NScan!")
return
try:
model = FastICA(n_components=NumFea, algorithm=Alg, max_iter=MaxIter, tol=Tol)
print("Running ICA Functional Alignment on Training Data ...")
OutData[ui.txtOTrData.text()] = model.fit_transform(XTr)
print("Running ICA Functional Alignment on Testing Data ...")
OutData[ui.txtOTeData.text()] = model.fit_transform(XTe)
except Exception as e:
print(str(e))
HAParam = dict()
HAParam["Method"] = "FastICA"
HAParam["NumFea"] = NumFea
HAParam["Algorithm"]= Alg
OutData["FunctionalAlignment"] = HAParam
OutData["Runtime"] = time.time() - tic
totalTime += OutData["Runtime"]
print("Saving ...")
mainIO_save(OutData, OutFile)
print("Fold " + str(fold_all) + " is DONE: " + OutFile)
print("Runtime: ", totalTime)
print("ICA Functional Alignment is done.")
msgBox.setText("ICA Functional Alignment is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
totalTime = 0
msgBox = QMessageBox()
TrFoldErr = list()
TeFoldErr = list()
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
if FoldTo < FoldFrom:
print("Please check fold parameters!")
return
for fold_all in range(FoldFrom, FoldTo + 1):
tic = time.time()
# Regularization
try:
Regularization = np.float(ui.txtRegularization.text())
except:
msgBox.setText("Regularization value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# OutFile
OutFile = ui.txtOutFile.text()
OutFile = OutFile.replace("$FOLD$", str(fold_all))
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
InFile = InFile.replace("$FOLD$", str(fold_all))
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrData.text()):
msgBox.setText("Please enter Output Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeData.text()):
msgBox.setText("Please enter Output Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
XTr = InData[ui.txtITrData.currentText()]
XTe = InData[ui.txtITeData.currentText()]
if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
XTr = preprocessing.scale(XTr)
XTe = preprocessing.scale(XTe)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
# NComponent
try:
NumFea = np.int32(ui.txtNumFea.text())
except:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea < 0:
msgBox.setText("Number of features must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(XTr)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea == 0:
NumFea = None
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrLabel.text()):
msgBox.setText(
"Please enter Train Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeLabel.text()):
msgBox.setText("Please enter Test Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrLabel.text()] = reshape_1Dvector(
InData[ui.txtITrLabel.currentText()])
OutData[ui.txtOTeLabel.text()] = reshape_1Dvector(
InData[ui.txtITeLabel.currentText()])
except:
print("Cannot load labels!")
# Subject
if not len(ui.txtITrSubject.currentText()):
msgBox.setText(
"Please enter Train Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeSubject.currentText()):
msgBox.setText(
"Please enter Test Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrSubject.text()):
msgBox.setText(
"Please enter Train Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeSubject.text()):
msgBox.setText(
"Please enter Test Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrSubject = InData[ui.txtITrSubject.currentText()]
OutData[ui.txtOTrSubject.text()] = reshape_1Dvector(TrSubject)
TeSubject = InData[ui.txtITeSubject.currentText()]
OutData[ui.txtOTeSubject.text()] = reshape_1Dvector(TeSubject)
except:
print("Cannot load Subject IDs")
return
# Task
if ui.cbTask.isChecked():
if not len(ui.txtITrTask.currentText()):
msgBox.setText(
"Please enter Input Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeTask.currentText()):
msgBox.setText(
"Please enter Input Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrTask.text()):
msgBox.setText(
"Please enter Output Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeTask.text()):
msgBox.setText(
"Please enter Output Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrTask = np.asarray(InData[ui.txtITrTask.currentText()])
OutData[ui.txtOTrTask.text()] = reshape_1Dvector(TrTask)
TeTask = np.asarray(InData[ui.txtITeTask.currentText()])
OutData[ui.txtOTeTask.text()] = reshape_1Dvector(TeTask)
TrTaskIndex = TrTask.copy()
for tasindx, tas in enumerate(np.unique(TrTask)):
TrTaskIndex[TrTask == tas] = tasindx + 1
TeTaskIndex = TeTask.copy()
for tasindx, tas in enumerate(np.unique(TeTask)):
TeTaskIndex[TeTask == tas] = tasindx + 1
except:
print("Cannot load Tasks!")
return
# Run
if ui.cbRun.isChecked():
if not len(ui.txtITrRun.currentText()):
msgBox.setText(
"Please enter Train Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeRun.currentText()):
msgBox.setText(
"Please enter Test Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrRun.text()):
msgBox.setText(
"Please enter Train Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeRun.text()):
msgBox.setText(
"Please enter Test Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrRun = InData[ui.txtITrRun.currentText()]
OutData[ui.txtOTrRun.text()] = reshape_1Dvector(TrRun)
TeRun = InData[ui.txtITeRun.currentText()]
OutData[ui.txtOTeRun.text()] = reshape_1Dvector(TeRun)
except:
print("Cannot load Runs!")
return
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtITrCounter.currentText()):
msgBox.setText(
"Please enter Train Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeCounter.currentText()):
msgBox.setText(
"Please enter Test Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrCounter.text()):
msgBox.setText(
"Please enter Train Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeCounter.text()):
msgBox.setText(
"Please enter Test Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrCounter = InData[ui.txtITrCounter.currentText()]
OutData[ui.txtOTrCounter.text()] = reshape_1Dvector(
TrCounter)
TeCounter = InData[ui.txtITeCounter.currentText()]
OutData[ui.txtOTeCounter.text()] = reshape_1Dvector(
TeCounter)
except:
print("Cannot load Counters!")
return
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtITrmLabel.currentText()):
msgBox.setText(
"Please enter Train Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITemLabel.currentText()):
msgBox.setText(
"Please enter Test Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrmLabel.text()):
msgBox.setText(
"Please enter Train Output Matrix Label variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTemLabel.text()):
msgBox.setText(
"Please enter Test Output Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrmLabel.text()] = InData[
ui.txtITrmLabel.currentText()]
OutData[ui.txtOTemLabel.text()] = InData[
ui.txtITemLabel.currentText()]
except:
print("Cannot load matrix lables!")
return
# Design
if ui.cbDM.isChecked():
if not len(ui.txtITrDM.currentText()):
msgBox.setText(
"Please enter Train Input Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeDM.currentText()):
msgBox.setText(
"Please enter Test Input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrDM.text()):
msgBox.setText(
"Please enter Train Output Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeDM.text()):
msgBox.setText(
"Please enter Test Output Design Matrix variable name!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrDM.text()] = InData[
ui.txtITrDM.currentText()]
OutData[ui.txtOTeDM.text()] = InData[
ui.txtITeDM.currentText()]
except:
print("Cannot load design matrices!")
return
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCol.text()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCol.text()] = InData[
ui.txtCol.currentText()]
except:
print("Cannot load coordinator!")
return
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCond.text()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCond.text()] = InData[
ui.txtCond.currentText()]
except:
print("Cannot load conditions!")
return
# FoldID
if ui.cbFoldID.isChecked():
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldID.text()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldID.text()] = reshape_1Dvector(
InData[ui.txtFoldID.currentText()])
except:
print("Cannot load Fold ID!")
return
# FoldInfo
if ui.cbFoldInfo.isChecked():
if not len(ui.txtFoldInfo.currentText()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldInfo.text()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldInfo.text()] = InData[
ui.txtFoldInfo.currentText()]
except:
print("Cannot load Fold Info!")
return
pass
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtITrScan.currentText()):
msgBox.setText(
"Please enter Number of Scan variable name for Input Train!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeScan.currentText()):
msgBox.setText(
"Please enter Number of Scan variable name for Input Test!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrScan.text()):
msgBox.setText(
"Please enter Number of Scan variable name for Output Train!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeScan.text()):
msgBox.setText(
"Please enter Number of Scan variable name for Output Test!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrScan.text()] = reshape_1Dvector(
InData[ui.txtITrScan.currentText()])
OutData[ui.txtOTeScan.text()] = reshape_1Dvector(
InData[ui.txtITeScan.currentText()])
except:
print("Cannot load NScan!")
return
# Train Analysis Level
print("Calculating Analysis Level for Training Set ...")
TrGroupFold = None
FoldStr = ""
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
TrGroupFold = TrSubject
FoldStr = "Subject"
else:
TrGroupFold = np.concatenate((TrSubject, TrRun))
FoldStr = "Subject+Run"
if ui.cbFTask.isChecked():
TrGroupFold = np.concatenate(
(TrGroupFold,
TrTaskIndex)) if TrGroupFold is not None else TrTaskIndex
FoldStr = FoldStr + "+Task"
if ui.cbFCounter.isChecked():
TrGroupFold = np.concatenate(
(TrGroupFold,
TrCounter)) if TrGroupFold is not None else TrCounter
FoldStr = FoldStr + "+Counter"
TrGroupFold = np.transpose(TrGroupFold)
TrUniqFold = np.array(
list(set(tuple(i) for i in TrGroupFold.tolist())))
TrFoldIDs = np.arange(len(TrUniqFold)) + 1
TrListFold = list()
for gfold in TrGroupFold:
for ufoldindx, ufold in enumerate(TrUniqFold):
if (ufold == gfold).all():
currentID = TrFoldIDs[ufoldindx]
break
TrListFold.append(currentID)
TrListFold = np.int32(TrListFold)
TrListFoldUniq = np.unique(TrListFold)
# Test Analysis Level
print("Calculating Analysis Level for Testing Set ...")
TeGroupFold = None
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
TeGroupFold = TeSubject
else:
TeGroupFold = np.concatenate((TeSubject, TeRun))
if ui.cbFTask.isChecked():
TeGroupFold = np.concatenate(
(TeGroupFold,
TeTaskIndex)) if TeGroupFold is not None else TeTaskIndex
if ui.cbFCounter.isChecked():
TeGroupFold = np.concatenate(
(TeGroupFold,
TeCounter)) if TeGroupFold is not None else TeCounter
TeGroupFold = np.transpose(TeGroupFold)
TeUniqFold = np.array(
list(set(tuple(i) for i in TeGroupFold.tolist())))
TeFoldIDs = np.arange(len(TeUniqFold)) + 1
TeListFold = list()
for gfold in TeGroupFold:
for ufoldindx, ufold in enumerate(TeUniqFold):
if (ufold == gfold).all():
currentID = TeFoldIDs[ufoldindx]
break
TeListFold.append(currentID)
TeListFold = np.int32(TeListFold)
TeListFoldUniq = np.unique(TeListFold)
# Train Partition
print("Partitioning Training Data ...")
TrX = list()
TrShape = None
for foldindx, fold in enumerate(TrListFoldUniq):
dat = XTr[np.where(TrListFold == fold)]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
dat = preprocessing.scale(dat)
print("Data belong to View " + str(foldindx + 1) +
" is scaled X~N(0,1).")
TrX.append(dat)
if TrShape is None:
TrShape = np.shape(dat)
else:
if not (TrShape == np.shape(dat)):
print("ERROR: Train, Reshape problem for Fold " +
str(foldindx + 1) + ", Shape: " +
str(np.shape(dat)))
return
print("Train: View " + str(foldindx + 1) +
" is extracted. Shape: " + str(np.shape(dat)))
print("Training Shape: " + str(np.shape(TrX)))
# Test Partition
print("Partitioning Testing Data ...")
TeX = list()
TeShape = None
for foldindx, fold in enumerate(TeListFoldUniq):
dat = XTe[np.where(TeListFold == fold)]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
dat = preprocessing.scale(dat)
print("Data belong to View " + str(foldindx + 1) +
" is scaled X~N(0,1).")
TeX.append(dat)
if TeShape is None:
TeShape = np.shape(dat)
else:
if not (TeShape == np.shape(dat)):
print("Test: Reshape problem for Fold " +
str(foldindx + 1))
return
print("Test: View " + str(foldindx + 1) + " is extracted.")
print("Testing Shape: " + str(np.shape(TeX)))
model = RHA(regularization=Regularization, Dim=NumFea)
print("Running Hyperalignment on Training Data ...")
model.fit(TrX)
G = model.get_G()
TrU = model.get_U()
print("Running Hyperalignment on Testing Data ...")
model.project(TeX)
TeU = model.get_U_tilde()
# Train Dot Product
print("Producting Training Data ...")
TrHX = None
TrErr = None
for foldindx, fold in enumerate(TrListFoldUniq):
mapping = np.dot(TrX[foldindx], TrU[foldindx])
TrErr = TrErr + (G -
mapping) if TrErr is not None else G - mapping
TrHX = np.concatenate(
(TrHX, mapping)) if TrHX is not None else mapping
OutData[ui.txtOTrData.text()] = TrHX
foldindx = foldindx + 1
TrErr = TrErr / foldindx
print("Train: alignment error ", np.linalg.norm(TrErr))
TrFoldErr.append(np.linalg.norm(TrErr))
# Train Dot Product
print("Producting Testing Data ...")
TeHX = None
TeErr = None
for foldindx, fold in enumerate(TeListFoldUniq):
mapping = np.dot(TeX[foldindx], TeU[foldindx])
TeErr = TeErr + (G -
mapping) if TeErr is not None else G - mapping
TeHX = np.concatenate(
(TeHX, mapping)) if TeHX is not None else mapping
OutData[ui.txtOTeData.text()] = TeHX
foldindx = foldindx + 1
TeErr = TeErr / foldindx
print("Test: alignment error ", np.linalg.norm(TeErr))
TeFoldErr.append(np.linalg.norm(TeErr))
HAParam = dict()
HAParam["Share"] = G
HAParam["Train"] = TrU
HAParam["Test"] = TeU
HAParam["Level"] = FoldStr
OutData["FunctionalAlignment"] = HAParam
OutData["Runtime"] = time.time() - tic
totalTime += OutData["Runtime"]
print("Saving ...")
mainIO_save(OutData, OutFile)
print("Fold " + str(fold_all) + " is DONE: " + OutFile)
print("Training -> Alignment Error: mean " + str(np.mean(TrFoldErr)) +
" std " + str(np.std(TrFoldErr)))
print("Testing -> Alignment Error: mean " + str(np.mean(TeFoldErr)) +
" std " + str(np.std(TeFoldErr)))
print("Runtime: ", totalTime)
print("Regularized Hyperalignment is done.")
msgBox.setText("Regularized Hyperalignment is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnInFile_click(self):
filename = LoadFile("Load data file ...",['Data files (*.ezx *.mat *.ezdata)'],'ezx',\
os.path.dirname(ui.txtInFile.text()))
if len(filename):
if os.path.isfile(filename):
try:
print("Loading ...")
data = mainIO_load(filename)
Keys = data.keys()
# Label
ui.txtLabel.clear()
HasDefualt = False
for key in Keys:
ui.txtLabel.addItem(key)
if key == "label":
HasDefualt = True
if HasDefualt:
ui.txtLabel.setCurrentText("label")
Labels = data[ui.txtLabel.currentText()]
Labels = np.unique(Labels)
print("Number of labels: ", np.shape(Labels)[0])
print("Labels: ", Labels)
# Subject
ui.txtSubject.clear()
HasDefualt = False
for key in Keys:
ui.txtSubject.addItem(key)
if key == "subject":
HasDefualt = True
if HasDefualt:
ui.txtSubject.setCurrentText("subject")
print("Number of subjects: ", np.shape(np.unique(data["subject"]))[0])
# Task
ui.txtTask.clear()
HasDefualt = False
for key in Keys:
ui.txtTask.addItem(key)
if key == "task":
HasDefualt = True
if HasDefualt:
ui.txtTask.setCurrentText("task")
# Run
ui.txtRun.clear()
HasDefualt = False
for key in Keys:
ui.txtRun.addItem(key)
if key == "run":
HasDefualt = True
if HasDefualt:
ui.txtRun.setCurrentText("run")
# Counter
ui.txtCounter.clear()
HasDefualt = False
for key in Keys:
ui.txtCounter.addItem(key)
if key == "counter":
HasDefualt = True
if HasDefualt:
ui.txtCounter.setCurrentText("counter")
ui.txtInFile.setText(filename)
ui.tbReport.clear()
ui.tbReport.setRowCount(0)
ui.tbReport.setColumnCount(0)
ui.tbReport2.clear()
ui.tbReport2.setRowCount(0)
ui.tbReport2.setColumnCount(0)
ui.tabWidget2.setCurrentIndex(0)
ui.tabWidget.setCurrentIndex(2)
except Exception as e:
print(e)
print("Cannot load data file!")
return
else:
print("File not found!")
def btnConvert_click(self):
totalTime = 0
msgBox = QMessageBox()
Model = ui.cbMethod.currentText()
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
if FoldTo < FoldFrom:
print("Please check fold parameters!")
return
for fold_all in range(FoldFrom, FoldTo+1):
tic = time.time()
# Regularization
try:
NIter = np.int32(ui.txtIter.text())
except:
msgBox.setText("Number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Gamma = np.float(ui.txtGamma.text())
except:
msgBox.setText("Gamma is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# AR Rank
try:
Rank = np.int32(ui.txtRank.text())
except:
msgBox.setText("Rank value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# AR Rank
try:
Rho = np.float(ui.txtRho.text())
except:
msgBox.setText("Rho value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# OutFile
OutFile = ui.txtOutFile.text()
OutFile = OutFile.replace("$FOLD$", str(fold_all))
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
InFile = InFile.replace("$FOLD$", str(fold_all))
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrData.text()):
msgBox.setText("Please enter Output Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeData.text()):
msgBox.setText("Please enter Output Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
XTr = InData[ui.txtITrData.currentText()]
XTe = InData[ui.txtITeData.currentText()]
if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
XTr = preprocessing.scale(XTr)
XTe = preprocessing.scale(XTe)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
# NComponent
try:
NumFea = np.int32(ui.txtNumFea.text())
except:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea < 0:
msgBox.setText("Number of features must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if NumFea > np.shape(XTr)[1]:
msgBox.setText("Number of features is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrLabel.text()):
msgBox.setText("Please enter Train Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeLabel.text()):
msgBox.setText("Please enter Test Output Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrLabel.text()] = reshape_1Dvector(InData[ui.txtITrLabel.currentText()])
OutData[ui.txtOTeLabel.text()] = reshape_1Dvector(InData[ui.txtITeLabel.currentText()])
except:
print("Cannot load labels!")
# Subject
if not len(ui.txtITrSubject.currentText()):
msgBox.setText("Please enter Train Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeSubject.currentText()):
msgBox.setText("Please enter Test Input Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrSubject.text()):
msgBox.setText("Please enter Train Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeSubject.text()):
msgBox.setText("Please enter Test Output Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrSubject = InData[ui.txtITrSubject.currentText()]
OutData[ui.txtOTrSubject.text()] = reshape_1Dvector(TrSubject)
TeSubject = InData[ui.txtITeSubject.currentText()]
OutData[ui.txtOTeSubject.text()] = reshape_1Dvector(TeSubject)
except:
print("Cannot load Subject IDs")
return
# Task
if ui.cbTask.isChecked():
if not len(ui.txtITrTask.currentText()):
msgBox.setText("Please enter Input Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeTask.currentText()):
msgBox.setText("Please enter Input Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrTask.text()):
msgBox.setText("Please enter Output Train Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeTask.text()):
msgBox.setText("Please enter Output Test Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrTask = np.asarray(InData[ui.txtITrTask.currentText()])
OutData[ui.txtOTrTask.text()] = reshape_1Dvector(TrTask)
TeTask = np.asarray(InData[ui.txtITeTask.currentText()])
OutData[ui.txtOTeTask.text()] = reshape_1Dvector(TeTask)
TrTaskIndex = TrTask.copy()
for tasindx, tas in enumerate(np.unique(TrTask)):
TrTaskIndex[TrTask == tas] = tasindx + 1
TeTaskIndex = TeTask.copy()
for tasindx, tas in enumerate(np.unique(TeTask)):
TeTaskIndex[TeTask == tas] = tasindx + 1
except:
print("Cannot load Tasks!")
return
# Run
if ui.cbRun.isChecked():
if not len(ui.txtITrRun.currentText()):
msgBox.setText("Please enter Train Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeRun.currentText()):
msgBox.setText("Please enter Test Input Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrRun.text()):
msgBox.setText("Please enter Train Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeRun.text()):
msgBox.setText("Please enter Test Output Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrRun = InData[ui.txtITrRun.currentText()]
OutData[ui.txtOTrRun.text()] = reshape_1Dvector(TrRun)
TeRun = InData[ui.txtITeRun.currentText()]
OutData[ui.txtOTeRun.text()] = reshape_1Dvector(TeRun)
except:
print("Cannot load Runs!")
return
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtITrCounter.currentText()):
msgBox.setText("Please enter Train Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeCounter.currentText()):
msgBox.setText("Please enter Test Input Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrCounter.text()):
msgBox.setText("Please enter Train Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeCounter.text()):
msgBox.setText("Please enter Test Output Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrCounter = InData[ui.txtITrCounter.currentText()]
OutData[ui.txtOTrCounter.text()] = reshape_1Dvector(TrCounter)
TeCounter = InData[ui.txtITeCounter.currentText()]
OutData[ui.txtOTeCounter.text()] = reshape_1Dvector(TeCounter)
except:
print("Cannot load Counters!")
return
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtITrmLabel.currentText()):
msgBox.setText("Please enter Train Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITemLabel.currentText()):
msgBox.setText("Please enter Test Input Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrmLabel.text()):
msgBox.setText("Please enter Train Output Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTemLabel.text()):
msgBox.setText("Please enter Test Output Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrmLabel.text()] = InData[ui.txtITrmLabel.currentText()]
OutData[ui.txtOTemLabel.text()] = InData[ui.txtITemLabel.currentText()]
except:
print("Cannot load matrix lables!")
return
# Design
if ui.cbDM.isChecked():
if not len(ui.txtITrDM.currentText()):
msgBox.setText("Please enter Train Input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeDM.currentText()):
msgBox.setText("Please enter Test Input Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrDM.text()):
msgBox.setText("Please enter Train Output Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeDM.text()):
msgBox.setText("Please enter Test Output Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrDM.text()] = InData[ui.txtITrDM.currentText()]
OutData[ui.txtOTeDM.text()] = InData[ui.txtITeDM.currentText()]
except:
print("Cannot load design matrices!")
return
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCol.text()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCol.text()] = InData[ui.txtCol.currentText()]
except:
print("Cannot load coordinator!")
return
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOCond.text()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOCond.text()] = InData[ui.txtCond.currentText()]
except:
print("Cannot load conditions!")
return
# FoldID
if ui.cbFoldID.isChecked():
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldID.text()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldID.text()] = reshape_1Dvector(InData[ui.txtFoldID.currentText()])
except:
print("Cannot load Fold ID!")
return
# FoldInfo
if ui.cbFoldInfo.isChecked():
if not len(ui.txtFoldInfo.currentText()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOFoldInfo.text()):
msgBox.setText("Please enter FoldInfo variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOFoldInfo.text()] = InData[ui.txtFoldInfo.currentText()]
except:
print("Cannot load Fold Info!")
return
pass
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtITrScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name for Input Train!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name for Input Test!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTrScan.text()):
msgBox.setText("Please enter Number of Scan variable name for Output Train!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtOTeScan.text()):
msgBox.setText("Please enter Number of Scan variable name for Output Test!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
OutData[ui.txtOTrScan.text()] = reshape_1Dvector(InData[ui.txtITrScan.currentText()])
OutData[ui.txtOTeScan.text()] = reshape_1Dvector(InData[ui.txtITeScan.currentText()])
except:
print("Cannot load NScan!")
return
# Train Analysis Level
print("Calculating Analysis Level for Training Set ...")
TrGroupFold = None
FoldStr = ""
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
TrGroupFold = TrSubject
FoldStr = "Subject"
else:
TrGroupFold = np.concatenate((TrSubject,TrRun))
FoldStr = "Subject+Run"
if ui.cbFTask.isChecked():
TrGroupFold = np.concatenate((TrGroupFold,TrTaskIndex)) if TrGroupFold is not None else TrTaskIndex
FoldStr = FoldStr + "+Task"
if ui.cbFCounter.isChecked():
TrGroupFold = np.concatenate((TrGroupFold,TrCounter)) if TrGroupFold is not None else TrCounter
FoldStr = FoldStr + "+Counter"
TrGroupFold = np.transpose(TrGroupFold)
TrUniqFold = np.array(list(set(tuple(i) for i in TrGroupFold.tolist())))
TrFoldIDs = np.arange(len(TrUniqFold)) + 1
TrListFold = list()
for gfold in TrGroupFold:
for ufoldindx, ufold in enumerate(TrUniqFold):
if (ufold == gfold).all():
currentID = TrFoldIDs[ufoldindx]
break
TrListFold.append(currentID)
TrListFold = np.int32(TrListFold)
TrListFoldUniq = np.unique(TrListFold)
# Test Analysis Level
print("Calculating Analysis Level for Testing Set ...")
TeGroupFold = None
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
TeGroupFold = TeSubject
else:
TeGroupFold = np.concatenate((TeSubject,TeRun))
if ui.cbFTask.isChecked():
TeGroupFold = np.concatenate((TeGroupFold,TeTaskIndex)) if TeGroupFold is not None else TeTaskIndex
if ui.cbFCounter.isChecked():
TeGroupFold = np.concatenate((TeGroupFold,TeCounter)) if TeGroupFold is not None else TeCounter
TeGroupFold = np.transpose(TeGroupFold)
TeUniqFold = np.array(list(set(tuple(i) for i in TeGroupFold.tolist())))
TeFoldIDs = np.arange(len(TeUniqFold)) + 1
TeListFold = list()
for gfold in TeGroupFold:
for ufoldindx, ufold in enumerate(TeUniqFold):
if (ufold == gfold).all():
currentID = TeFoldIDs[ufoldindx]
break
TeListFold.append(currentID)
TeListFold = np.int32(TeListFold)
TeListFoldUniq = np.unique(TeListFold)
# Train Partition
print("Partitioning Training Data ...")
TrX = list()
TrShape = None
for foldindx, fold in enumerate(TrListFoldUniq):
dat = XTr[np.where(TrListFold == fold)]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
dat = preprocessing.scale(dat)
print("Data belong to View " + str(foldindx + 1) + " is scaled X~N(0,1).")
AR_MAT = AR(np.shape(dat)[0], rho=Rho, rank=Rank)
TrX.append(np.dot(np.transpose(dat), AR_MAT))
if TrShape is None:
TrShape = np.shape(dat)
else:
if not(TrShape == np.shape(dat)):
print("ERROR: Train, Reshape problem for Fold " + str(foldindx + 1) + ", Shape: " + str(np.shape(dat)))
return
print("Train: View " + str(foldindx + 1) + " is extracted. Shape: " + str(np.shape(dat)))
print("Training Shape (sub x voxel x time): " + str(np.shape(TrX)))
# Test Partition
print("Partitioning Testing Data ...")
TeX = list()
TeShape = None
for foldindx, fold in enumerate(TeListFoldUniq):
dat = XTe[np.where(TeListFold == fold)]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
dat = preprocessing.scale(dat)
print("Data belong to View " + str(foldindx + 1) + " is scaled X~N(0,1).")
AR_MAT = AR(np.shape(dat)[0], rho=Rho, rank=Rank)
TeX.append(np.dot(np.transpose(dat), AR_MAT))
if TeShape is None:
TeShape = np.shape(dat)
else:
if not(TeShape == np.shape(dat)):
print("Test: Reshape problem for Fold " + str(foldindx + 1))
return
print("Test: View " + str(foldindx + 1) + " is extracted.")
print("Testing Shape (sub x voxel x time): " + str(np.shape(TeX)))
if NumFea == 0:
NumFea = np.min(np.shape(TrX)[1:3])
print("Number of features are automatically selected as ", NumFea)
MessageString = ""
SharedR = None
try:
if Model == "Probabilistic SRM":
model = SRM(n_iter=NIter,features=NumFea)
MessageString = "Probabilistic "
elif Model == "Deterministic SRM":
model = DetSRM(n_iter=NIter,features=NumFea)
MessageString = "Deterministic "
else:
model = RSRM(n_iter=NIter, features=NumFea, gamma=Gamma)
SharedR = True
MessageString = "Robust "
print("Running Hyperalignment on Training Data ...")
model.fit(TrX)
if SharedR == True:
S = model.r_
Spec_train = list()
for spec in model.s_:
Spec_train.append(np.transpose(spec))
else:
S = model.s_
WTr = list()
for wtri in model.w_ :
WTr.append(wtri)
# Train Dot Product
print("Producting Training Data ...")
TrHX = None
for mapping, viewi in zip(WTr, TrX):
TrHX = np.concatenate((TrHX, np.transpose(np.dot(np.transpose(mapping),viewi)))) if TrHX is not None else np.transpose(np.dot(np.transpose(mapping),viewi))
OutData[ui.txtOTrData.text()] = TrHX
print("Running Hyperalignment on Testing Data ...")
TeHX = None
WTe = list()
if SharedR:
Specific_test = np.zeros(np.shape(TeX))
for ijk in range(NIter):
# Considering Specific_Test is fixed and Updating Wi
WTe = list()
for vid, view in enumerate(TeX):
product = np.dot(view - Specific_test[vid], np.transpose(S))
U, _, V = lg.svd(product, full_matrices=False, check_finite=False)
WTe.append(np.dot(U, V))
# Considering Wi is fixed and Updating Specific_test
Specific_test = list()
for vid, (view, Wtest) in enumerate(zip(TeX, WTe)):
NewSpec = view - np.dot(Wtest, S)
posIndex = NewSpec > Gamma
negIndex = NewSpec < -Gamma
NewSpec[posIndex] -= Gamma
NewSpec[negIndex] += Gamma
NewSpec[np.logical_and(~posIndex, ~negIndex)] = .0
Specific_test.append(NewSpec)
# Generating the concatenate results
Spec_test = list()
for (Wtest, spec) in zip(WTe, Specific_test):
TeHX = np.concatenate((TeHX, np.transpose(np.dot(np.transpose(Wtest), view - spec)))) if TeHX is not None else np.transpose(np.dot(np.transpose(Wtest),view - spec))
Spec_test.append(np.transpose(spec))
else:
for vid, view in enumerate(TeX):
product = np.dot(view, np.transpose(S))
U, _, V = lg.svd(product, full_matrices=False, check_finite=False)
Wtest = np.dot(U,V)
WTe.append(Wtest)
TeHX = np.concatenate((TeHX, np.transpose(np.dot(np.transpose(Wtest),view)))) if TeHX is not None else np.transpose(np.dot(np.transpose(Wtest),view))
OutData[ui.txtOTeData.text()] = TeHX
except Exception as e:
print(e)
HAParam = dict()
if ui.cbSaveShare.isChecked():
HAParam["Share"] = S
if SharedR:
HAParam["Specific_train"] = Spec_train
HAParam["Specific_test"] = Spec_test
if ui.cbSaveMap.isChecked():
HAParam["WTrain"] = WTr
HAParam["WTest"] = WTe
HAParam["Model"] = Model
OutData["FunctionalAlignment"] = HAParam
OutData["Runtime"] = time.time() - tic
totalTime += OutData["Runtime"]
print("Saving ...")
mainIO_save(OutData, OutFile)
print("Fold " + str(fold_all) + " is DONE: " + OutFile)
print("Runtime: ", totalTime)
print(MessageString + "Autoregressive Shared Response Model is done.")
msgBox.setText(MessageString + "Autoregressive Shared Response Model is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnInFile_click(self):
filename = LoadFile("Load data file ...",['Data files (*.ezx *.mat *.ezdata)'],'ezx',\
os.path.dirname(ui.txtInFile.text()))
if len(filename):
if os.path.isfile(filename):
try:
print("Loading ...")
data = mainIO_load(filename)
Keys = data.keys()
# Data
ui.txtData.clear()
HasDefualt = False
for key in Keys:
ui.txtData.addItem(key)
if key == "data":
HasDefualt = True
if HasDefualt:
ui.txtData.setCurrentText("data")
print("Data Shape: ", np.shape(data["data"]))
# Label
ui.txtLabel.clear()
HasDefualt = False
for key in Keys:
ui.txtLabel.addItem(key)
if key == "label":
HasDefualt = True
if HasDefualt:
ui.txtLabel.setCurrentText("label")
Labels = data[ui.txtLabel.currentText()]
Labels = np.unique(Labels)
print("Number of labels: ", np.shape(Labels)[0])
print("Labels:")
print(Labels)
ui.txtClass.clear()
for lbl in Labels:
ui.txtClass.append(str(lbl))
# Design
ui.txtDesign.clear()
HasDefualt = False
for key in Keys:
ui.txtDesign.addItem(key)
if key == "design":
HasDefualt = True
if HasDefualt:
ui.txtDesign.setCurrentText("design")
# Condition
ui.txtCond.clear()
HasDefualt = False
for key in Keys:
ui.txtCond.addItem(key)
if key == "condition":
HasDefualt = True
if HasDefualt:
ui.txtCond.setCurrentText("condition")
# Subject
ui.txtSubject.clear()
HasDefualt = False
for key in Keys:
ui.txtSubject.addItem(key)
if key == "subject":
HasDefualt = True
if HasDefualt:
ui.txtSubject.setCurrentText("subject")
# Run
ui.txtRun.clear()
HasDefualt = False
for key in Keys:
ui.txtRun.addItem(key)
if key == "run":
HasDefualt = True
if HasDefualt:
ui.txtRun.setCurrentText("run")
# Counter
ui.txtCounter.clear()
HasDefualt = False
for key in Keys:
ui.txtCounter.addItem(key)
if key == "counter":
HasDefualt = True
if HasDefualt:
ui.txtCounter.setCurrentText("counter")
# Task
ui.txtTask.clear()
HasDefualt = False
for key in Keys:
ui.txtTask.addItem(key)
if key == "task":
HasDefualt = True
if HasDefualt:
ui.txtTask.setCurrentText("task")
ui.txtInFile.setText(filename)
print("DONE.")
except Exception as e:
print(e)
print("Cannot load data file!")
return
else:
print("File not found!")
def btnConvert_click(self):
msgBox = QMessageBox()
tStart = time.time()
try:
Threshold = np.float(ui.txtThre.text())
assert Threshold <= 1
assert Threshold >= 0
except:
msgBox.setText("Threshold is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
EdgeThreshold = np.float(ui.txtEdgeThre.text())
assert EdgeThreshold <= 1
assert EdgeThreshold >= 0
except:
msgBox.setText("Edge threshold is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
MetricCode = ui.txtMetric.toPlainText()
allvars = dict(locals(), **globals())
exec(MetricCode, allvars, allvars)
Metric = allvars['metric']
except Exception as e:
msgBox.setText("Metric is wrong!\n" + str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
IntegrationCode = ui.txtIntegration.toPlainText()
allvars = dict(locals(), **globals())
exec(IntegrationCode, allvars, allvars)
Integration = allvars['integration']
except Exception as e:
msgBox.setText("Integration is wrong!\n" + str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
AtlasPath = ui.txtAtlasPath.text()
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace(
"[", "").replace("]", "").split()
Filter = np.int32(Filter)
except:
print("Filter is wrong!")
return
# Region Filter
try:
RegionFilter = ui.txtRegions.text()
if not len(RegionFilter):
RegionFilter = []
else:
RegionFilter = RegionFilter.replace("\'", " ").replace(
",", " ").replace("[", "").replace("]", "").split()
RegionFilter = np.int32(RegionFilter)
except:
print("Region filter is wrong!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData = dict()
# AtlasFile
AtlasFile = ui.txtAtlasFile.text()
if not len(AtlasFile):
msgBox.setText("Please enter atlas file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(AtlasFile):
msgBox.setText("Atlas file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
AtlasHDR = nb.load(AtlasFile)
AtlasImg = np.asanyarray(AtlasHDR.dataobj)
AtlasReg = np.unique(AtlasImg)
if not 0 in RegionFilter:
AtlasReg = AtlasReg[np.where(AtlasReg != 0)[0]]
print("Region 0 is considered as rest mode!")
AtlasShape = np.shape(AtlasImg)
except:
msgBox.setText("Cannot load atlas file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
print("Loading ...")
InData = mainIO_load(InFile)
imgShape = InData["imgShape"][0]
except:
print("Cannot load data!")
return
try:
assert imgShape[0] == AtlasShape[0]
assert imgShape[1] == AtlasShape[1]
assert imgShape[2] == AtlasShape[2]
except:
msgBox.setText("Input file (" +
str(imgShape).replace("]", "").replace("[", "") +
") and Atlas file " +
str(AtlasShape).replace(",", "") +
" must have the same shape!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Data
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Input Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Train Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Condition
try:
if not len(ui.txtCond.currentText()):
Cond = 0
else:
Cond = InData[ui.txtCond.currentText()]
OutData[ui.txtCond.currentText()] = Cond
labels = list()
for con in Cond:
labels.append(reshape_condition_cell(con[1]))
labels = np.array(labels)
except:
msgBox.setText("Condition value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
X = InData[ui.txtData.currentText()]
L = InData[ui.txtLabel.currentText()][0]
if ui.cbScale.isChecked():
X = preprocessing.scale(X)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data or label")
return
try:
if len(ui.txtCoord.currentText()):
Coord = np.transpose(InData[ui.txtCoord.currentText()])
else:
Coord = None
except:
msgBox.setText("Coordinate variable is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(L == fil)[0]
X = np.delete(X, labelIndx, axis=0)
L = np.delete(L, labelIndx, axis=0)
print("Class ID = " + str(fil) + " is removed from data.")
Net, ThrNet, ActiveRegions, A, ACoord, listL = ClassicNetworkAnalysis(
X=X,
L=L,
Coord=Coord,
Integration=Integration,
Metric=Metric,
AtlasImg=AtlasImg,
affine=AtlasHDR.affine,
KeepRegions=RegionFilter,
AtlasPath=AtlasPath,
Threshold=Threshold)
Out = {}
Out["Networks"] = Net
Out["ThresholdNetworks"] = ThrNet
Out["ActiveRegions"] = ActiveRegions
Out["Atlas"] = A
Out["Atlas_parcellation"] = ACoord
Out["Atlas_affine"] = AtlasHDR.affine
Out["condition"] = Cond
Out["labels"] = listL
Out["RunTime"] = time.time() - tStart
print("Runtime (s): %f" % (Out["RunTime"]))
print("Saving results ...")
mainIO_save(Out, OutFile)
print("Output is saved.")
if ui.cbDiagram.isChecked():
for nnIndex, (nn, tnn) in enumerate(zip(Net, ThrNet)):
try:
Title = f"Label: {reshape_condition_cell(Cond[nnIndex][1])}"
except:
Title = f"Label: {nnIndex}"
PlotConnectome(tnn, ACoord, Title, EdgeThreshold)
fig = plt.figure()
plt.imshow(np.transpose(nn))
plt.title(Title)
plt.show()
print("DONE.")
msgBox.setText("Network analysis is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
# Subject
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Subject = InData[ui.txtSubject.currentText()]
OutData[ui.txtOSubject.text()] = reshape_1Dvector(Subject)
except:
print("Cannot load Subject ID")
return
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
X = InData[ui.txtData.currentText()]
if not ui.rbScale.isChecked():
X_new = preprocessing.scale(X)
print("Whole of data is scaled X~N(0,1).")
else:
print("Partition data to subject level ...")
SubjectUniq = np.unique(Subject)
X_Sub = list()
for subj in SubjectUniq:
X_Sub.append(preprocessing.scale(X[np.where(Subject == subj)[1], :]))
print("Data in subject level is scaled, X_" + str(subj) + "~N(0,1).")
lenPCA = len(X_Sub)
print("Data integration ... ")
X_new = None
for xsubindx, xsub in enumerate(X_Sub):
X_new = np.concatenate((X_new, xsub)) if X_new is not None else xsub
print("Integration: ", xsubindx + 1, " of ", lenPCA, " is done.")
OutData[ui.txtOData.text()] = X_new
except:
print("Cannot load data")
return
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOLabel.text()] = reshape_1Dvector(InData[ui.txtLabel.currentText()])
# Task
if ui.cbTask.isChecked():
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOTask.text()] = reshape_1Dvector(InData[ui.txtTask.currentText()])
# Run
if ui.cbRun.isChecked():
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtORun.text()] = reshape_1Dvector(InData[ui.txtRun.currentText()])
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCounter.text()] = reshape_1Dvector(InData[ui.txtCounter.currentText()])
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtmLabel.currentText()):
msgBox.setText("Please enter Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOmLabel.text()] = InData[ui.txtmLabel.currentText()]
# Design
if ui.cbDM.isChecked():
if not len(ui.txtDM.currentText()):
msgBox.setText("Please enter Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtODM.text()] = InData[ui.txtDM.currentText()]
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCol.text()] = InData[ui.txtCol.currentText()]
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCond.text()] = InData[ui.txtCond.currentText()]
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOScan.text()] = reshape_1Dvector(InData[ui.txtScan.currentText()])
print("Saving ...")
mainIO_save(OutData, ui.txtOutFile.text())
#io.savemat(ui.txtOutFile.text(), mdict=OutData)
print("DONE.")
msgBox.setText("Normalization is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnInFile_click(self):
filename = LoadFile("Load data file ...",['Data files (*.ezx *.mat *.ezdata)'],'ezx',\
os.path.dirname(ui.txtInFile.text()))
if len(filename):
if os.path.isfile(filename):
try:
data = mainIO_load(filename)
Keys = data.keys()
# Train Filter
ui.txtFilterTrID.clear()
for key in Keys:
ui.txtFilterTrID.addItem(key)
# Test Filter
ui.txtFilterTeID.clear()
for key in Keys:
ui.txtFilterTeID.addItem(key)
# Train Data
ui.txtITrData.clear()
HasDefualt = False
for key in Keys:
ui.txtITrData.addItem(key)
if key == "train_data":
HasDefualt = True
if HasDefualt:
ui.txtITrData.setCurrentText("train_data")
# Test Data
ui.txtITeData.clear()
HasDefualt = False
for key in Keys:
ui.txtITeData.addItem(key)
if key == "test_data":
HasDefualt = True
if HasDefualt:
ui.txtITeData.setCurrentText("test_data")
# Train Label
ui.txtITrLabel.clear()
HasDefualt = False
for key in Keys:
ui.txtITrLabel.addItem(key)
if key == "train_label":
HasDefualt = True
if HasDefualt:
ui.txtITrLabel.setCurrentText("train_label")
# Test Label
ui.txtITeLabel.clear()
ui.txtClass.clear()
HasDefualt = False
for key in Keys:
ui.txtITeLabel.addItem(key)
if key == "test_label":
HasDefualt = True
if HasDefualt:
ui.txtITeLabel.setCurrentText("test_label")
# set number of features
Labels = data[ui.txtITrLabel.currentText()]
Labels = np.unique(Labels)
for lbl in Labels:
ui.txtClass.append(str(lbl))
# FoldID
ui.txtFoldID.clear()
HasDefualt = False
for key in Keys:
ui.txtFoldID.addItem(key)
if key == "FoldID":
HasDefualt = True
if HasDefualt:
ui.txtFoldID.setCurrentText("FoldID")
ui.lbFoldID.setText(
"ID=" + str(data[ui.txtFoldID.currentText()][0][0]))
# Coordinate
ui.txtCol.clear()
HasDefualt = False
for key in Keys:
ui.txtCol.addItem(key)
if key == "coordinate":
HasDefualt = True
if HasDefualt:
ui.txtCol.setCurrentText("coordinate")
# ImgShape
ui.txtImg.clear()
HasDefualt = False
for key in Keys:
ui.txtImg.addItem(key)
if key == "imgShape":
HasDefualt = True
if HasDefualt:
ui.txtImg.setCurrentText("imgShape")
try:
print(
f'Input image shape is {tuple(data["imgShape"][0])}'
)
except:
print("Cannot find ImgShape")
ui.txtInFile.setText(filename)
except Exception as e:
print(e)
print("Cannot load data file!")
return
else:
print("File not found!")
def btnInFile_click(self):
filename = LoadFile("Load data file ...",['Data files (*.ezx *.mat *.ezdata)'],'ezx',\
os.path.dirname(ui.txtInFile.text()))
if len(filename):
if os.path.isfile(filename):
try:
data = mainIO_load(filename)
Keys = data.keys()
# Data
ui.txtData.clear()
HasDefualt = False
for key in Keys:
ui.txtData.addItem(key)
if key == "data":
HasDefualt = True
if HasDefualt:
ui.txtData.setCurrentText("data")
# Label
ui.txtLabel.clear()
HasDefualt = False
for key in Keys:
ui.txtLabel.addItem(key)
if key == "label":
HasDefualt = True
if HasDefualt:
ui.txtLabel.setCurrentText("label")
# mLabel
ui.txtmLabel.clear()
HasDefualt = False
for key in Keys:
ui.txtmLabel.addItem(key)
if key == "mlabel":
HasDefualt = True
if HasDefualt:
ui.txtmLabel.setCurrentText("mlabel")
ui.cbmLabel.setChecked(HasDefualt)
# Coordinate
ui.txtCol.clear()
HasDefualt = False
for key in Keys:
ui.txtCol.addItem(key)
if key == "coordinate":
HasDefualt = True
if HasDefualt:
ui.txtCol.setCurrentText("coordinate")
ui.cbCol.setChecked(HasDefualt)
# Design
ui.txtDM.clear()
HasDefualt = False
for key in Keys:
ui.txtDM.addItem(key)
if key == "design":
HasDefualt = True
if HasDefualt:
ui.txtDM.setCurrentText("design")
ui.cbDM.setChecked(HasDefualt)
# Subject
ui.txtSubject.clear()
HasDefualt = False
for key in Keys:
ui.txtSubject.addItem(key)
if key == "subject":
HasDefualt = True
if HasDefualt:
ui.txtSubject.setCurrentText("subject")
# Task
ui.txtTask.clear()
HasDefualt = False
for key in Keys:
ui.txtTask.addItem(key)
if key == "task":
HasDefualt = True
if HasDefualt:
ui.txtTask.setCurrentText("task")
ui.cbTask.setChecked(HasDefualt)
# Run
ui.txtRun.clear()
HasDefualt = False
for key in Keys:
ui.txtRun.addItem(key)
if key == "run":
HasDefualt = True
if HasDefualt:
ui.txtRun.setCurrentText("run")
ui.cbRun.setChecked(HasDefualt)
# Counter
ui.txtCounter.clear()
HasDefualt = False
for key in Keys:
ui.txtCounter.addItem(key)
if key == "counter":
HasDefualt = True
if HasDefualt:
ui.txtCounter.setCurrentText("counter")
ui.cbCounter.setChecked(HasDefualt)
# Condition
ui.txtCond.clear()
HasDefualt = False
for key in Keys:
ui.txtCond.addItem(key)
if key == "condition":
HasDefualt = True
if HasDefualt:
ui.txtCond.setCurrentText("condition")
ui.cbCond.setChecked(HasDefualt)
# NScan
ui.txtScan.clear()
HasDefualt = False
for key in Keys:
ui.txtScan.addItem(key)
if key == "nscan":
HasDefualt = True
if HasDefualt:
ui.txtScan.setCurrentText("nscan")
ui.cbNScan.setChecked(HasDefualt)
# set number of features
XShape = np.shape(data[ui.txtData.currentText()])
print("Data shape: ", XShape)
if len(XShape) != 4:
msgBox = QMessageBox()
msgBox.setText(
"This method just supports 4D datasets!")
msgBox.setIcon(QMessageBox.Warning)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
else:
print("Data shape is okay.")
ui.txtInFile.setText(filename)
except Exception as e:
print(e)
print("Cannot load data file!")
return
else:
print("File not found!")
def btnConvert_click(self):
msgBox = QMessageBox()
print("Loading...")
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Atlas
InAtlas = ui.txtAtlas.text()
if not len(InAtlas):
msgBox.setText("Please enter atlas file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InAtlas):
msgBox.setText("Atlas file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
AtlasHdr = nb.load(InAtlas)
AtlasImg = np.asanyarray(AtlasHdr.dataobj)
AtlasShape = np.shape(AtlasImg)
if np.shape(AtlasShape)[0] != 3:
msgBox.setText("Atlas must be 3D image")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
except:
msgBox.setText("Cannot load atlas file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace(
"[", "").replace("]", "").split()
Filter = np.int32(Filter)
except:
print("Class Filter is wrong!")
msgBox.setText("Class filter is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
MappingVectorRegion = None
CodeText = ui.txtEvents.toPlainText()
eCodeText = ui.txtEvents2.toPlainText()
Fold = list()
accuracy = list()
precision = list()
average_precision = list()
f1score = list()
recall = list()
accuracyTr = list()
precisionTr = list()
average_precisionTr = list()
f1scoreTr = list()
recallTr = list()
InFileList = list()
OutData = dict()
OutData["ModelAnalysis"] = "Atlas-based ensemble"
for fold in range(FoldFrom, FoldTo + 1):
print(f"Analyzing Fold: {fold}...")
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText(f"FOLD {fold}: Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InFile = InFile.replace("$FOLD$", str(fold))
InFileList.append(InFile)
if not os.path.isfile(InFile):
msgBox.setText(f"FOLD {fold}: Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Load InData
try:
InData = mainIO_load(InFile)
except:
print(f"FOLD {fold}: Cannot load file: {InFile}")
return False
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrX = InData[ui.txtITrData.currentText()]
except:
print(f"FOLD {fold}: Cannot load train data")
msgBox.setText(f"FOLD {fold}: Cannot load train data!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TeX = InData[ui.txtITeData.currentText()]
except:
print(f"FOLD {fold}: Cannot load test data")
msgBox.setText(f"FOLD {fold}: Cannot load train data!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TrL = InData[ui.txtITrLabel.currentText()][0]
except:
print(f"FOLD {fold}: Cannot load label")
return
try:
TeL = InData[ui.txtITeLabel.currentText()][0]
except:
print(f"FOLD {fold}: Cannot load test label")
return
# Ref Filter Train
if ui.cbFilterTrID.isChecked():
try:
# Create content structure for training
TrFilterContent = ui.txtFilterTrContent.text()
TrFilterContent = TrFilterContent.replace(" ", "")
if not len(TrFilterContent):
print("Reference filter for training is wrong!")
msgBox.setText(
"Reference filter for training is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
else:
TrFilterContent = TrFilterContent.replace(
"\'",
" ").replace(",",
" ").replace("[",
"").replace("]",
"").split()
TrFilterContent = np.int32(TrFilterContent)
# Check Filter ID for training
if not len(ui.txtFilterTrID.currentText()):
msgBox.setText(
"Please enter variable name for training filter!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TrF = InData[ui.txtFilterTrID.currentText()][0]
if np.shape(TrX)[0] != np.shape(TrF)[0] or np.shape(
TrL)[0] != np.shape(TrF)[0]:
print(
"Shape of reference for training must be the same as data and label"
)
msgBox.setText(
"Shape of reference for training must be the same as data and label"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
except:
print("Reference filter for training is wrong!")
msgBox.setText("Reference filter for training is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Remove training set
try:
print(
"Removing training set based on reference. Data shape: ",
np.shape(TrX), "Label shape: ", np.shape(TrL))
for content in TrFilterContent:
contIndex = np.where(TrF == content)[0]
TrL = np.delete(TrL, contIndex, axis=0)
TrX = np.delete(TrX, contIndex, axis=0)
TrF = np.delete(TrF, contIndex, axis=0)
print(" Content", content,
"is removed from training set. Data shape: ",
np.shape(TrX), "Label shape: ", np.shape(TrL))
except Exception as e:
print("Cannot filter the training set based on Reference")
print(str(e))
msgBox.setText(
"Cannot filter the training set based on Reference")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Ref Filter Test
if ui.cbFilterTeID.isChecked():
try:
# Create content structure for testing
TeFilterContent = ui.txtFilterTeContent.text()
TeFilterContent = TeFilterContent.replace(" ", "")
if not len(TeFilterContent):
print("Reference filter for testing is wrong!")
msgBox.setText(
"Reference filter for testing is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
else:
TeFilterContent = TeFilterContent.replace(
"\'",
" ").replace(",",
" ").replace("[",
"").replace("]",
"").split()
TeFilterContent = np.int32(TeFilterContent)
# Check Filter ID for testing
if not len(ui.txtFilterTeID.currentText()):
msgBox.setText(
"Please enter variable name for testing filter!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TeF = InData[ui.txtFilterTeID.currentText()][0]
if np.shape(TeX)[0] != np.shape(TeF)[0] or np.shape(
TeL)[0] != np.shape(TeF)[0]:
print(
"Shape of reference for testing must be the same as data and label"
)
msgBox.setText(
"Shape of reference for testing must be the same as data and label"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
except:
print("Reference filter for testing is wrong!")
msgBox.setText("Reference filter for testing is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Remove testing set
try:
print(
"Removing testing set based on reference. Data shape: ",
np.shape(TeX), "Label shape: ", np.shape(TeL))
for content in TeFilterContent:
contIndex = np.where(TeF == content)[0]
TeL = np.delete(TeL, contIndex, axis=0)
TeX = np.delete(TeX, contIndex, axis=0)
TeF = np.delete(TeF, contIndex, axis=0)
print(" Content", content,
"is removed from testing set. Data shape: ",
np.shape(TeX), "Label shape: ", np.shape(TeL))
except Exception as e:
print("Cannot filter the testing set based on Reference")
print(str(e))
msgBox.setText(
"Cannot filter the testing set based on Reference")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
try:
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(TrL == fil)[0]
TrL = np.delete(TrL, labelIndx, axis=0)
TrX = np.delete(TrX, labelIndx, axis=0)
# Remove Testing Set
labelIndx = np.where(TeL == fil)[0]
TeL = np.delete(TeL, labelIndx, axis=0)
TeX = np.delete(TeX, labelIndx, axis=0)
print("Class ID = " + str(fil) +
" is removed from data.")
if ui.cbScale.isChecked():
TrX = preprocessing.scale(TrX)
TeX = preprocessing.scale(TeX)
print(
"Whole of data is scaled Train~N(0,1) and Test~N(0,1)."
)
except:
print("Cannot load data or label")
return
# FoldID
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
currFID = InData[ui.txtFoldID.currentText()][0][0]
Fold.append(currFID)
except:
print("Cannot load Fold ID!")
return
# Creating Mapping Dictionary for Atlas
if MappingVectorRegion is None:
# Coordinate
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Coord = np.transpose(InData[ui.txtCol.currentText()])
except:
print("Cannot load coordinate")
return
# imgShape
if not len(ui.txtImg.currentText()):
msgBox.setText("Please enter Image Shape variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
DataShape = tuple(InData[ui.txtImg.currentText()][0])
except:
print("Cannot load data image shape")
return
# Check shape
if AtlasShape[0] != DataShape[0] or AtlasShape[1] != DataShape[
1] or AtlasShape[2] != DataShape[2]:
print(
f'Atlas and data must have the same shape.\nAtlas: {AtlasShape} vs. Data: {DataShape}'
)
msgBox.setText(
f'Atlas and data must have the same shape.\nAtlas: {AtlasShape} vs. Data: {DataShape}'
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Mapping 2D vector space to 3D region area...")
MappingVectorRegion = dict()
for cooIdx, coo in enumerate(Coord):
reg = AtlasImg[coo[0], coo[1], coo[2]]
if reg == 0:
print(f'{tuple(coo)} is belong to black area.')
else:
try:
MappingVectorRegion[str(reg)].append(cooIdx)
except:
MappingVectorRegion[str(reg)] = list()
MappingVectorRegion[str(reg)].append(cooIdx)
print(
f'{tuple(coo)}:{cooIdx} is belong to region {reg}')
print(f"FOLD {fold}: analyzing regions...")
baseTrX = list()
baseTeX = list()
for reg in sorted(MappingVectorRegion.keys()):
RegIndex = MappingVectorRegion[reg]
SubXtr = TrX[:, RegIndex]
SubXte = TeX[:, RegIndex]
try:
allvars = dict(locals(), **globals())
exec(CodeText, allvars, allvars)
model = allvars['model']
model.fit(SubXtr, TrL)
baseTrX.append(model.predict(SubXtr))
TeP = model.predict(SubXte)
baseTeX.append(TeP)
bacc = accuracy_score(TeL, TeP)
except Exception as e:
print(f'Cannot generate model\n{e}')
msgBox.setText(f'Cannot generate model\n{e}')
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print(f"FOLD {fold}: Linear accuracy for {reg} is {bacc}")
baseTrX = np.transpose(baseTrX)
baseTeX = np.transpose(baseTeX)
try:
print(f"FOLD {fold}: Creating ensemble model ...")
allvars = dict(locals(), **globals())
exec(eCodeText, allvars, allvars)
emodel = allvars['emodel']
emodel.fit(baseTrX, TrL)
PrL = emodel.predict(baseTrX)
PeL = emodel.predict(baseTeX)
except Exception as e:
print(f"Cannot generate the final model\n{e}")
msgBox.setText(f"Cannot generate the final model\n{e}")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.cbAverage.isChecked():
acc = accuracy_score(TeL, PeL)
accTr = accuracy_score(TrL, PrL)
accuracy.append(acc)
accuracyTr.append(accTr)
print(
"FoldID = {:d}, Average Train {:5.2f} Test {:5.2f}"
.format(currFID, accTr * 100, acc * 100))
if ui.cbPrecision.isChecked():
pre = precision_score(TeL,
PeL,
average=ui.cbPrecisionAvg.currentData())
preTr = precision_score(
TrL, PrL, average=ui.cbPrecisionAvg.currentData())
precision.append(pre)
precisionTr.append(preTr)
print(
"FoldID = {:d}, Precision Train {:5.2f} Test {:5.2f}"
.format(currFID, preTr * 100, pre * 100))
if ui.cbAPrecision.isChecked():
prA = average_precision_score(
TeL, PeL, average=ui.cbAPrecisionAvg.currentData())
prATr = average_precision_score(
TrL, PrL, average=ui.cbAPrecisionAvg.currentData())
average_precision.append(prA)
average_precisionTr.append(prATr)
print(
"FoldID = {:d}, Average Precision: Train {:5.2f} Test {:5.2f}"
.format(currFID, prATr * 100, prA * 100))
if ui.cbRecall.isChecked():
rec = recall_score(TeL,
PeL,
average=ui.cbRecallAvg.currentData())
recTr = recall_score(TrL,
PrL,
average=ui.cbRecallAvg.currentData())
recall.append(rec)
recallTr.append(recTr)
print(
"FoldID = {:d}, Recall: Train {:5.2f} Test {:5.2f}"
.format(currFID, recTr * 100, rec * 100))
if ui.cbF1.isChecked():
f1 = f1_score(TeL, PeL, average=ui.cbF1Avg.currentData())
f1Tr = f1_score(TrL, PrL, average=ui.cbF1Avg.currentData())
f1score.append(f1)
f1scoreTr.append(f1Tr)
print(
"FoldID = {:d}, F1: Train {:5.2f} Test {:5.2f}"
.format(currFID, f1Tr * 100, f1 * 100))
print("FoldID = " + str(currFID) + " is analyzed!")
if ui.cbAverage.isChecked():
OutData["FoldAccuracy"] = accuracy
MeanAcc = np.mean(accuracy)
OutData["MeanTestAccuracy"] = MeanAcc
STDAcc = np.std(accuracy)
OutData["StdTestAccuracy"] = STDAcc
MeanAccTr = np.mean(accuracyTr)
OutData["MeanTrainAccuracy"] = MeanAccTr
STDAccTr = np.std(accuracyTr)
OutData["StdTrainAccuracy"] = STDAccTr
print(
"Accuracy: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanAccTr * 100, STDAccTr, MeanAcc * 100, STDAcc))
if ui.cbPrecision.isChecked():
OutData["ModePrecision"] = ui.cbPrecisionAvg.currentText()
OutData["FoldPrecision"] = precision
MeanPre = np.mean(precision)
OutData["MeanTrainPrecision"] = MeanPre
STDPre = np.std(precision)
OutData["StdTrainPrecision"] = STDPre
MeanPreTr = np.mean(precisionTr)
OutData["MeanTestPrecision"] = MeanPreTr
STDPreTr = np.std(precisionTr)
OutData["StdTestPrecision"] = STDPreTr
print(
"Precision: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanPreTr * 100, STDPreTr, MeanPre * 100, STDPre))
if ui.cbAPrecision.isChecked():
OutData["ModeAveragePrecision"] = ui.cbAPrecisionAvg.currentText()
OutData["FoldAveragePrecision"] = average_precision
MeanAPre = np.mean(average_precision)
OutData["MeanTrainAveragePrecision"] = MeanAPre
STDAPre = np.std(average_precision)
OutData["StdTestAveragePrecision"] = STDAPre
MeanAPreTr = np.mean(average_precisionTr)
OutData["MeanTrainAveragePrecision"] = MeanAPreTr
STDAPreTr = np.std(average_precisionTr)
OutData["StdTrainAveragePrecision"] = STDAPreTr
print(
"AveragePrecision: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanAPreTr * 100, STDAPreTr, MeanAPre * 100, STDAPre))
if ui.cbRecall.isChecked():
OutData["ModeRecall"] = ui.cbRecallAvg.currentText()
OutData["FoldRecall"] = recall
MeanRec = np.mean(recall)
OutData["MeanTestRecall"] = MeanRec
STDRec = np.std(recall)
OutData["StdTestRecall"] = STDRec
MeanRecTr = np.mean(recallTr)
OutData["MeanTrainRecall"] = MeanRecTr
STDRecTr = np.std(recallTr)
OutData["StdTrainRecall"] = STDRecTr
print(
"Recall: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanRecTr * 100, STDRecTr, MeanRec * 100, STDRec))
if ui.cbF1.isChecked():
OutData["ModeF1"] = ui.cbF1Avg.currentText()
OutData["FoldF1"] = f1score
MeanF1 = np.mean(f1score)
OutData["MeanTestF1"] = MeanF1
STDF1 = np.std(f1score)
OutData["StdTestF1"] = STDF1
MeanF1Tr = np.mean(f1scoreTr)
OutData["MeanTrainF1"] = MeanF1Tr
STDF1Tr = np.std(f1scoreTr)
OutData["StdTrainF1"] = STDF1Tr
print(
"F1: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanF1Tr * 100, STDF1Tr, MeanF1 * 100, STDF1))
OutData["InputFiles"] = InFileList
print("Saving ...")
mainIO_save(OutData, ui.txtOutFile.text())
print("DONE.")
msgBox.setText("Atlas-based ensemble analysis is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnInFile_click(self):
msgBox = QMessageBox()
filename = LoadFile("Load data file ...",['Data files (*.ezx *.mat *.ezdata)'],'ezx',\
os.path.dirname(ui.txtInFile.text()))
if len(filename):
if os.path.isfile(filename):
try:
data = mainIO_load(filename)
Keys = data.keys()
ui.lbMatrix.setText("Image Shape=" +
str(data["imgShape"][0]))
cooShape = None
dataShape = None
# Train Data
ui.txtMatrix.clear()
HasDefualt = None
for key in Keys:
ui.txtMatrix.addItem(key)
if key == "train_data" or key == "data" or key == "Beta" or key == "sharedvoxelspace":
HasDefualt = key
dataShape = np.shape(data[key])
ui.txtTime.setText("[0-" + str(dataShape[0] - 1) +
"]")
if HasDefualt is not None:
ui.txtMatrix.setCurrentText(HasDefualt)
# Test Data
ui.txtCoord.clear()
HasDefualt = False
for key in Keys:
ui.txtCoord.addItem(key)
if key == "coordinate":
ui.txtCoord.setCurrentText("coordinate")
cooShape = np.shape(data[key][0])[0]
ui.lbCoord.setText("ROI Size=" + str(cooShape))
if not cooShape == dataShape[1]:
print(
"WARNING: Coordinate size is not matched by data size!"
)
msgBox.setText(
"Coordinate size is not matched by data size!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
if cooShape is None:
print("WARNING: Cannot find coordinate!")
if dataShape is None:
print("WARNING: Cannot find data matrix!")
ui.txtInFile.setText(filename)
except Exception as e:
print(e)
print("Cannot load data file!")
return
else:
print("File not found!")
def btnShowFilterContent_click(self):
msgBox = QMessageBox()
filename = ui.txtInFile.text()
if len(filename):
if os.path.isfile(filename):
try:
InData = mainIO_load(filename)
if ui.cbFilterTrID.isChecked():
try:
# Check Filter ID for training
if not len(ui.txtFilterTrID.currentText()):
msgBox.setText(
"Please enter variable name for training filter!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TrF = InData[ui.txtFilterTrID.currentText()][0]
fstr = ""
fconnect = ""
for filter in np.unique(TrF):
fstr += fconnect
fstr += str(filter)
fconnect = ", "
print("Training Reference Content:", fstr)
except:
print("Reference filter for training is wrong!")
msgBox.setText(
"Reference filter for training is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Ref Filter Test
if ui.cbFilterTeID.isChecked():
try:
# Check Filter ID for testing
if not len(ui.txtFilterTeID.currentText()):
msgBox.setText(
"Please enter variable name for testing filter!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TeF = InData[ui.txtFilterTeID.currentText()][0]
fstr = ""
fconnect = ""
for filter in np.unique(TeF):
fstr += fconnect
fstr += str(filter)
fconnect = ", "
print("Testing Reference Content:", fstr)
except:
print("Reference filter for testing is wrong!")
msgBox.setText(
"Reference filter for testing is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
except Exception as e:
print(e)
print("Cannot load data file!")
return
else:
print("File not found!")
def btnConvert_click(self):
msgBox = QMessageBox()
# CType
CType = ui.cbCType.currentData()
# IParams
IParams = ui.cbIParams.currentData()
# Warm Start
WStart = ui.cbWarmStart.isChecked()
# NCluster
try:
NComponent = np.int32(ui.txtNComponent.text())
except:
msgBox.setText("Number of Component is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# NInit
try:
NInit = np.int32(ui.txtNInit.text())
except:
msgBox.setText("Number of init is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Tol
try:
Tol = np.float(ui.txtTole.text())
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# MaxIter
try:
MaxIter = np.int32(ui.txtMaxIter.text())
except:
msgBox.setText("Max Iteration is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Regularization
try:
Regularization = np.float(ui.txtRegularization.text())
except:
msgBox.setText("Regularization is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Verbose
try:
Verbose = np.int(ui.txtVerbose.text())
except:
msgBox.setText("Verbose is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# IVerbose
try:
IVerbose = np.int(ui.txtIVerbose.text())
except:
msgBox.setText("Verbose Interval is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace(
"[", "").replace("]", "").split()
Filter = np.int32(Filter)
except:
print("Filter is wrong!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData = dict()
OutData["ModelAnalysis"] = "Gaussian Mixture"
# OutModel
OutModel = ui.txtOutModel.text()
if not len(OutModel):
OutModel = None
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
# Data
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
X = InData[ui.txtData.currentText()]
L = InData[ui.txtLabel.currentText()][0]
try:
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(L == fil)[0]
L = np.delete(L, labelIndx, axis=0)
X = np.delete(X, labelIndx, axis=0)
print("Class ID = " + str(fil) + " is removed from data.")
if ui.cbScale.isChecked():
X = preprocessing.scale(X)
print("Whole of data is scaled to N(0,1).")
except:
print("Cannot load data or label")
return
try:
cls = GaussianMixture(n_components=NComponent,covariance_type=CType,tol=Tol,reg_covar=Regularization,\
max_iter=MaxIter,n_init=NInit,init_params=IParams,\
warm_start=WStart,verbose=Verbose,verbose_interval=IVerbose)
print("Run Clustering ...")
cls.fit(X)
PeL = cls.predict(X)
except Exception as e:
print(e)
msgBox = QMessageBox()
msgBox.setText(str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
OutData["predict"] = PeL
if OutModel is not None:
joblib.dump(cls, OutModel)
print("Model is saved: " + OutModel)
if ui.cbAverage.isChecked():
acc = accuracy_score(L, PeL)
OutData["Accuracy"] = acc
print("Average {:5.2f}".format(acc *
100))
if ui.cbNMI.isChecked():
NMI = normalized_mutual_info_score(L, PeL)
OutData["NMI"] = NMI
print("Normalized Mutual Information (NMI) {:7.6f}".format(NMI))
if ui.cbRIA.isChecked():
RIA = adjusted_rand_score(L, PeL)
OutData["RIA"] = RIA
print("Rand Index Adjusted (RIA) {:7.6f}".format(RIA))
if ui.cbAMI.isChecked():
AMI = adjusted_mutual_info_score(L, PeL)
OutData["AMI"] = AMI
print("Adjusted Mutual Information (AMI) {:7.6f}".format(AMI))
print("Saving ...")
mainIO_save(OutData, OutFile)
print("DONE.")
msgBox.setText("Gaussian Mixture Clustering is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
tme = time.time()
msgBox = QMessageBox()
try:
FoldFrom = np.int32(ui.txtFoldFrom.text())
FoldTo = np.int32(ui.txtFoldTo.text())
except:
print("Please check fold parameters!")
return
if FoldTo < FoldFrom:
print("Please check fold parameters!")
return
# Shuffle
Shuffle = ui.cbShuffle.isChecked()
# FitIntercept
FitIntercept = ui.cbFitIntercept.isChecked()
# WarmStart
WarmStart = ui.cbWarmStart.isChecked()
# NesterovsMomentum
AverageParm = ui.cbAverageParm.isChecked()
# Loss
Loss = ui.cbLoss.currentText()
# Penalty
Penalty = ui.cbPenalty.currentText()
# Learning Rate
LearningRate = ui.cbLearningRate.currentText()
# Alpha
try:
Alpha = np.float(ui.txtAlpha.text())
except:
msgBox.setText("Alpha is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# L1Rate
try:
L1Rate = np.float(ui.txtL1Rate.text())
except:
msgBox.setText("L1 Rate is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Eta0
try:
Eta0 = np.double(ui.txtEta0.text())
except:
msgBox.setText("Eta 0 Rate is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Powert
try:
Powert = np.double(ui.txtPowert.text())
except:
msgBox.setText("Power_t is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Tol
try:
Tol = np.float(ui.txtTol.text())
if Tol == 0:
Tol = None
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# MaxIte
try:
MaxIter = np.int32(ui.txtMaxIter.text())
if MaxIter <= 0:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
except:
msgBox.setText("Maximum number of iterations is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Epsilon
try:
Epsilon = np.float(ui.txtEpsilon.text())
except:
msgBox.setText("Epsilon is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Epoach
try:
NIterNoChange = np.int32(ui.txtEpochs.text())
if NIterNoChange <= 0:
msgBox.setText("N Iteration No Change must be positive!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
except:
msgBox.setText("N Iteration No Change is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Verbose
try:
Verbose = np.int32(ui.txtVerbose.text())
except:
msgBox.setText("Verbose is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# NJob
try:
NJobs = np.int32(ui.txtNJobs.text())
if NJobs <= 0:
NJobs = -1
except:
msgBox.setText("n_jobs is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace(
"[", "").replace("]", "").split()
Filter = np.int32(Filter)
except:
print("Class filter is wrong!")
msgBox.setText("Class filter is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
Fold = list()
accuracy = list()
precision = list()
average_precision = list()
f1score = list()
recall = list()
accuracyTr = list()
precisionTr = list()
average_precisionTr = list()
f1scoreTr = list()
recallTr = list()
InFileList = list()
OutData = dict()
OutData[
"ModelAnalysis"] = "Stochastic Gradient Descent based Classifier"
for fold in range(FoldFrom, FoldTo + 1):
# OutModel
OutModel = ui.txtOutModel.text()
if not len(OutModel):
OutModel = None
else:
OutModel = OutModel.replace("$FOLD$", str(fold))
# InFile
InFile = ui.txtInFile.text()
InFile = InFile.replace("$FOLD$", str(fold))
InFileList.append(InFile)
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
# Data
if not len(ui.txtITrData.currentText()):
msgBox.setText("Please enter Input Train Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeData.currentText()):
msgBox.setText("Please enter Input Test Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtITrLabel.currentText()):
msgBox.setText("Please enter Train Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not len(ui.txtITeLabel.currentText()):
msgBox.setText("Please enter Test Input Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TrX = InData[ui.txtITrData.currentText()]
TeX = InData[ui.txtITeData.currentText()]
TrL = InData[ui.txtITrLabel.currentText()][0]
TeL = InData[ui.txtITeLabel.currentText()][0]
# Ref Filter Train
if ui.cbFilterTrID.isChecked():
try:
# Create content structure for training
TrFilterContent = ui.txtFilterTrContent.text()
TrFilterContent = TrFilterContent.replace(" ", "")
if not len(TrFilterContent):
print("Reference filter for training is wrong!")
msgBox.setText(
"Reference filter for training is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
else:
TrFilterContent = TrFilterContent.replace(
"\'",
" ").replace(",",
" ").replace("[",
"").replace("]",
"").split()
TrFilterContent = np.int32(TrFilterContent)
# Check Filter ID for training
if not len(ui.txtFilterTrID.currentText()):
msgBox.setText(
"Please enter variable name for training filter!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TrF = InData[ui.txtFilterTrID.currentText()][0]
if np.shape(TrX)[0] != np.shape(TrF)[0] or np.shape(
TrL)[0] != np.shape(TrF)[0]:
print(
"Shape of reference for training must be the same as data and label"
)
msgBox.setText(
"Shape of reference for training must be the same as data and label"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
except:
print("Reference filter for training is wrong!")
msgBox.setText("Reference filter for training is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Remove training set
try:
print(
"Removing training set based on reference. Data shape: ",
np.shape(TrX), "Label shape: ", np.shape(TrL))
for content in TrFilterContent:
contIndex = np.where(TrF == content)[0]
TrL = np.delete(TrL, contIndex, axis=0)
TrX = np.delete(TrX, contIndex, axis=0)
TrF = np.delete(TrF, contIndex, axis=0)
print(" Content", content,
"is removed from training set. Data shape: ",
np.shape(TrX), "Label shape: ", np.shape(TrL))
except Exception as e:
print("Cannot filter the training set based on Reference")
print(str(e))
msgBox.setText(
"Cannot filter the training set based on Reference")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Ref Filter Test
if ui.cbFilterTeID.isChecked():
try:
# Create content structure for testing
TeFilterContent = ui.txtFilterTeContent.text()
TeFilterContent = TeFilterContent.replace(" ", "")
if not len(TeFilterContent):
print("Reference filter for testing is wrong!")
msgBox.setText(
"Reference filter for testing is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
else:
TeFilterContent = TeFilterContent.replace(
"\'",
" ").replace(",",
" ").replace("[",
"").replace("]",
"").split()
TeFilterContent = np.int32(TeFilterContent)
# Check Filter ID for testing
if not len(ui.txtFilterTeID.currentText()):
msgBox.setText(
"Please enter variable name for testing filter!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TeF = InData[ui.txtFilterTeID.currentText()][0]
if np.shape(TeX)[0] != np.shape(TeF)[0] or np.shape(
TeL)[0] != np.shape(TeF)[0]:
print(
"Shape of reference for testing must be the same as data and label"
)
msgBox.setText(
"Shape of reference for testing must be the same as data and label"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
except:
print("Reference filter for testing is wrong!")
msgBox.setText("Reference filter for testing is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# Remove testing set
try:
print(
"Removing testing set based on reference. Data shape: ",
np.shape(TeX), "Label shape: ", np.shape(TeL))
for content in TeFilterContent:
contIndex = np.where(TeF == content)[0]
TeL = np.delete(TeL, contIndex, axis=0)
TeX = np.delete(TeX, contIndex, axis=0)
TeF = np.delete(TeF, contIndex, axis=0)
print(" Content", content,
"is removed from testing set. Data shape: ",
np.shape(TeX), "Label shape: ", np.shape(TeL))
except Exception as e:
print("Cannot filter the testing set based on Reference")
print(str(e))
msgBox.setText(
"Cannot filter the testing set based on Reference")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
try:
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(TrL == fil)[0]
TrL = np.delete(TrL, labelIndx, axis=0)
TrX = np.delete(TrX, labelIndx, axis=0)
# Remove Testing Set
labelIndx = np.where(TeL == fil)[0]
TeL = np.delete(TeL, labelIndx, axis=0)
TeX = np.delete(TeX, labelIndx, axis=0)
print("Class ID = " + str(fil) +
" is removed from data.")
if ui.cbScale.isChecked():
TrX = preprocessing.scale(TrX)
TeX = preprocessing.scale(TeX)
print(
"Whole of data is scaled Train~N(0,1) and Test~N(0,1)."
)
except:
print("Cannot load data or label")
return
# FoldID
if not len(ui.txtFoldID.currentText()):
msgBox.setText("Please enter FoldID variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
currFID = InData[ui.txtFoldID.currentText()][0][0]
Fold.append(currFID)
except:
print("Cannot load Fold ID!")
return
try:
clf = SGDClassifier(loss=Loss,penalty=Penalty,alpha=Alpha,l1_ratio=L1Rate,fit_intercept=FitIntercept,\
max_iter=MaxIter,tol=Tol,shuffle=Shuffle,verbose=Verbose,epsilon=Epsilon,\
n_jobs=NJobs,learning_rate=LearningRate,eta0=Eta0,power_t=Powert,\
warm_start=WarmStart,average=AverageParm,n_iter_no_change=NIterNoChange)
print("FoldID = " + str(currFID) + " is training ...")
clf.fit(TrX, TrL)
if OutModel is not None:
joblib.dump(clf, OutModel)
print("FoldID = " + str(currFID) + " Model is saved: " +
OutModel)
print("FoldID = " + str(currFID) + " is testing ...")
PeL = clf.predict(TeX)
PrL = clf.predict(TrX)
OutData["fold" + str(currFID) +
"_confusion_matrix"] = confusion_matrix(
TeL, PeL, np.unique(TeL))
OutData["fold" + str(currFID) +
"_classification_report"] = classification_report(
TeL, PeL)
print(OutData["fold" + str(currFID) +
"_classification_report"])
except Exception as e:
msgBox.setText(str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.cbAverage.isChecked():
acc = accuracy_score(TeL, PeL)
accTr = accuracy_score(TrL, PrL)
accuracy.append(acc)
accuracyTr.append(accTr)
print(
"FoldID = {:d}, Average Train {:5.2f} Test {:5.2f}"
.format(currFID, accTr * 100, acc * 100))
if ui.cbPrecision.isChecked():
pre = precision_score(TeL,
PeL,
average=ui.cbPrecisionAvg.currentData())
preTr = precision_score(
TrL, PrL, average=ui.cbPrecisionAvg.currentData())
precision.append(pre)
precisionTr.append(preTr)
print(
"FoldID = {:d}, Precision Train {:5.2f} Test {:5.2f}"
.format(currFID, preTr * 100, pre * 100))
if ui.cbAPrecision.isChecked():
prA = average_precision_score(
TeL, PeL, average=ui.cbAPrecisionAvg.currentData())
prATr = average_precision_score(
TrL, PrL, average=ui.cbAPrecisionAvg.currentData())
average_precision.append(prA)
average_precisionTr.append(prATr)
print(
"FoldID = {:d}, Average Precision: Train {:5.2f} Test {:5.2f}"
.format(currFID, prATr * 100, prA * 100))
if ui.cbRecall.isChecked():
rec = recall_score(TeL,
PeL,
average=ui.cbRecallAvg.currentData())
recTr = recall_score(TrL,
PrL,
average=ui.cbRecallAvg.currentData())
recall.append(rec)
recallTr.append(recTr)
print(
"FoldID = {:d}, Recall: Train {:5.2f} Test {:5.2f}"
.format(currFID, recTr * 100, rec * 100))
if ui.cbF1.isChecked():
f1 = f1_score(TeL, PeL, average=ui.cbF1Avg.currentData())
f1Tr = f1_score(TrL, PrL, average=ui.cbF1Avg.currentData())
f1score.append(f1)
f1scoreTr.append(f1Tr)
print(
"FoldID = {:d}, F1: Train {:5.2f} Test {:5.2f}"
.format(currFID, f1Tr * 100, f1 * 100))
print("FoldID = " + str(currFID) + " is analyzed!")
if ui.cbAverage.isChecked():
OutData["FoldAccuracy"] = accuracy
MeanAcc = np.mean(accuracy)
OutData["MeanTestAccuracy"] = MeanAcc
STDAcc = np.std(accuracy)
OutData["StdTestAccuracy"] = STDAcc
MeanAccTr = np.mean(accuracyTr)
OutData["MeanTrainAccuracy"] = MeanAccTr
STDAccTr = np.std(accuracyTr)
OutData["StdTrainAccuracy"] = STDAccTr
print(
"Accuracy: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanAccTr * 100, STDAccTr, MeanAcc * 100, STDAcc))
if ui.cbPrecision.isChecked():
OutData["ModePrecision"] = ui.cbPrecisionAvg.currentText()
OutData["FoldPrecision"] = precision
MeanPre = np.mean(precision)
OutData["MeanTrainPrecision"] = MeanPre
STDPre = np.std(precision)
OutData["StdTrainPrecision"] = STDPre
MeanPreTr = np.mean(precisionTr)
OutData["MeanTestPrecision"] = MeanPreTr
STDPreTr = np.std(precisionTr)
OutData["StdTestPrecision"] = STDPreTr
print(
"Precision: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanPreTr * 100, STDPreTr, MeanPre * 100, STDPre))
if ui.cbAPrecision.isChecked():
OutData["ModeAveragePrecision"] = ui.cbAPrecisionAvg.currentText()
OutData["FoldAveragePrecision"] = average_precision
MeanAPre = np.mean(average_precision)
OutData["MeanTrainAveragePrecision"] = MeanAPre
STDAPre = np.std(average_precision)
OutData["StdTestAveragePrecision"] = STDAPre
MeanAPreTr = np.mean(average_precisionTr)
OutData["MeanTrainAveragePrecision"] = MeanAPreTr
STDAPreTr = np.std(average_precisionTr)
OutData["StdTrainAveragePrecision"] = STDAPreTr
print(
"AveragePrecision: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanAPreTr * 100, STDAPreTr, MeanAPre * 100, STDAPre))
if ui.cbRecall.isChecked():
OutData["ModeRecall"] = ui.cbRecallAvg.currentText()
OutData["FoldRecall"] = recall
MeanRec = np.mean(recall)
OutData["MeanTestRecall"] = MeanRec
STDRec = np.std(recall)
OutData["StdTestRecall"] = STDRec
MeanRecTr = np.mean(recallTr)
OutData["MeanTrainRecall"] = MeanRecTr
STDRecTr = np.std(recallTr)
OutData["StdTrainRecall"] = STDRecTr
print(
"Recall: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanRecTr * 100, STDRecTr, MeanRec * 100, STDRec))
if ui.cbF1.isChecked():
OutData["ModeF1"] = ui.cbF1Avg.currentText()
OutData["FoldF1"] = f1score
MeanF1 = np.mean(f1score)
OutData["MeanTestF1"] = MeanF1
STDF1 = np.std(f1score)
OutData["StdTestF1"] = STDF1
MeanF1Tr = np.mean(f1scoreTr)
OutData["MeanTrainF1"] = MeanF1Tr
STDF1Tr = np.std(f1scoreTr)
OutData["StdTrainF1"] = STDF1Tr
print(
"F1: Train {:5.2f} +/- {:4.2f} Test {:5.2f} +/- {:4.2f}"
.format(MeanF1Tr * 100, STDF1Tr, MeanF1 * 100, STDF1))
OutData["InputFiles"] = InFileList
OutData["Runtime"] = time.time() - tme
print("Runtime: ", OutData["Runtime"])
print("Saving ...")
mainIO_save(OutData, OutFile)
print("DONE.")
msgBox.setText("Stochastic Gradient Descent based Classifier is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
ThresholdType = ui.cbThType.currentData()
MinTh = None
MaxTh = None
if (ThresholdType == "min") or (ThresholdType == "ext"):
try:
MinTh = np.double(ui.txtThMin.text())
except:
msgBox.setText("Min Threshold must be a number")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Min Threshold is valid")
if (ThresholdType == "max") or (ThresholdType == "ext"):
try:
MaxTh = np.double(ui.txtThMax.text())
except:
msgBox.setText("Max Threshold must be a number")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Max Threshold is valid")
# OutFile
AFNI = ui.txtAFNI.text()
if not len(AFNI):
AFNI = None
else:
CopyFile = ui.txtFAFNI.text()
if not len(CopyFile):
msgBox.setText("Please select 3dcopy command!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
if not os.path.isfile(CopyFile):
msgBox.setText("Please select 3dcopy command!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
RefitFile = ui.txtFSUMA.text()
if not len(RefitFile):
msgBox.setText("Please select 3drefit command!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
if not os.path.isfile(RefitFile):
msgBox.setText("Please select 3drefit command!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
if not len(ui.txtMatrix.currentText()):
msgBox.setText("Matrix name not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
if not len(ui.txtCoord.currentText()):
msgBox.setText("Coordinate name not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
if not len(ui.txtTime.text()):
msgBox.setText("Time points not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
AffineFile = ui.txtSSSpace.currentText()
if not len(AffineFile):
msgBox.setText("Please enter affine reference!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
if not os.path.isfile(AffineFile):
msgBox.setText("Affine reference not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
AffineHDR = nb.load(AffineFile)
Affine = AffineHDR.affine
Time = strRange(ui.txtTime.text())
if Time is None:
msgBox.setText("Time points is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
try:
InData = mainIO_load(InFile)
Mat = InData[ui.txtMatrix.currentText()]
Coord = InData[ui.txtCoord.currentText()]
imgShape = InData["imgShape"][0]
except:
print("Cannot load data!")
msgBox.setText("Cannot load data!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return
ImgData = None
# Filter Mat
for t in Time:
vox = Mat[t, :]
if ui.cbScale.isChecked():
vox = preprocessing.scale(vox)
print("Time Point: " + str(t) + " is normalized.")
if MinTh is not None:
vox[np.where(vox < MinTh)] = 0
print("Time Point: " + str(t) +
", Lower band thresholding is done!")
if MaxTh is not None:
vox[np.where(vox > MaxTh)] = 0
print("Time Point: " + str(t) +
", Upper band thresholding is done!")
img = np.zeros([imgShape[0], imgShape[1], imgShape[2], 1])
for coIndx, _ in enumerate(Coord[0]):
img[Coord[0][coIndx], Coord[1][coIndx], Coord[2][coIndx],
0] = vox[coIndx]
ImgData = img if ImgData is None else np.concatenate(
(ImgData, img), axis=3)
print("Time point: " + str(t) + " is done.")
print("Saving image ...")
NIF = nb.Nifti1Image(ImgData, Affine)
nb.save(NIF, OutFile)
if AFNI is not None:
cmd = CopyFile + " " + OutFile + " " + AFNI
print("Running: " + cmd)
os.system(cmd)
cmd = RefitFile + " -view tlrc -space MNI " + AFNI + " " + AFNI + "+tlrc."
print("Running: " + cmd)
os.system(cmd)
print("DONE!")
msgBox.setText("Image file is generated.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
try:
Threshold = np.float64(ui.txtThreshold.text())
except:
msgBox.setText("Standard deviation threshold value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
net_model = createModel(ui.tbModel)
except Exception as e:
msgBox.setText(str(e))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if net_model is None:
msgBox.setText("Please create a network model!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if ui.rbScale.isChecked() == True and ui.rbALScale.isChecked(
) == False:
msgBox.setText(
"Subject Level Normalization is just available for Subject Level Analysis!"
)
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
InData = mainIO_load(InFile)
OutData = dict()
OutData["imgShape"] = reshape_1Dvector(InData["imgShape"])
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
X = InData[ui.txtData.currentText()]
if ui.cbScale.isChecked() and (not ui.rbScale.isChecked()):
X = X - X.mean()
X = X / X.std()
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data")
return
# Subject
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Subject = InData[ui.txtSubject.currentText()]
OutData[ui.txtOSubject.text()] = reshape_1Dvector(Subject)
except:
print("Cannot load Subject ID")
return
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOLabel.text()] = reshape_1Dvector(
InData[ui.txtLabel.currentText()])
# Task
if ui.cbTask.isChecked():
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOTask.text()] = reshape_1Dvector(
InData[ui.txtTask.currentText()])
# Run
if ui.cbRun.isChecked():
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtORun.text()] = reshape_1Dvector(
InData[ui.txtRun.currentText()])
# Counter
if ui.cbCounter.isChecked():
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCounter.text()] = reshape_1Dvector(
InData[ui.txtCounter.currentText()])
# Matrix Label
if ui.cbmLabel.isChecked():
if not len(ui.txtmLabel.currentText()):
msgBox.setText("Please enter Matrix Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOmLabel.text()] = InData[ui.txtmLabel.currentText()]
# Design
if ui.cbDM.isChecked():
if not len(ui.txtDM.currentText()):
msgBox.setText("Please enter Design Matrix variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtODM.text()] = InData[ui.txtDM.currentText()]
# Coordinate
if ui.cbCol.isChecked():
if not len(ui.txtCol.currentText()):
msgBox.setText("Please enter Coordinator variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCol.text()] = InData[ui.txtCol.currentText()]
# Condition
if ui.cbCond.isChecked():
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOCond.text()] = InData[ui.txtCond.currentText()]
# Number of Scan
if ui.cbNScan.isChecked():
if not len(ui.txtScan.currentText()):
msgBox.setText("Please enter Number of Scan variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData[ui.txtOScan.text()] = reshape_1Dvector(
InData[ui.txtScan.currentText()])
if ui.rbALScale.isChecked():
print("Partition data to subject level ...")
SubjectUniq = np.unique(Subject)
X_Sub = list()
for subj in SubjectUniq:
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
extractXsub = X[np.where(Subject == subj)[1], :]
extractXsub = extractXsub - extractXsub.mean()
extractXsub = extractXsub / extractXsub.std()
X_Sub.append(extractXsub)
print("Data in subject level is scaled, X_" + str(subj) +
"~N(0,1).")
else:
X_Sub.append(X[np.where(Subject == subj)[1], :])
print("Subject ", subj, " is extracted from data.")
print("Running CNN in subject level ...")
X_Sub_PCA = list()
lenPCA = len(X_Sub)
for xsubindx, xsub in enumerate(X_Sub):
net = CNN(net_model)
if ui.rbMat.isChecked():
xsub_tran = net.tonumpy(
net.vectorize(
net(torch.Tensor(np.expand_dims(xsub, axis=1)))))
else:
xsub_tran = net.tonumpy(
net(torch.Tensor(np.expand_dims(xsub, axis=1))))
X_Sub_PCA.append(xsub_tran)
print("CNN: ", xsubindx + 1, " of ", lenPCA,
" is done. Shape: " + str(np.shape(xsub_tran)))
print("Data integration ... ")
X_new = None
for xsubindx, xsub in enumerate(X_Sub_PCA):
X_new = np.concatenate(
(X_new, xsub)) if X_new is not None else xsub
print("Integration: ", xsubindx + 1, " of ", lenPCA,
" is done.")
if not ui.rbMat.isChecked():
X_new = X_new[:, 0, :, :, :]
# Apply Thresholding
print("Data shape before thresholding: ", np.shape(X_new))
X_new_th = X_new
if ui.rbMat.isChecked() and ui.cbThreshold.isChecked():
index = np.where(np.std(X_new, axis=0) >= Threshold)
X_new_index = X_new[:, index]
print("Standard deviation thresholding ...")
X_new_th = None
for xx_new in X_new_index:
X_new_th = xx_new if X_new_th is None else np.concatenate(
(X_new_th, xx_new), axis=0)
# Apply Normalization
if ui.rbMat.isChecked() and ui.cbOutNormal.isChecked():
X_new_th = X_new_th - np.mean(X_new_th)
X_new_th = X_new_th / np.std(X_new_th)
print("Output data is normalized!")
OutData[ui.txtOData.text()] = X_new_th
else:
print("Running CNN ...")
net = CNN(net_model)
if ui.rbMat.isChecked():
X_new = net.tonumpy(
net.vectorize(net(torch.Tensor(np.expand_dims(X,
axis=1)))))
else:
X_new = net.tonumpy(
net(torch.Tensor(np.expand_dims(X, axis=1))))
print("CNN is done. Shape: " + str(np.shape(X_new)))
# Apply Thresholding
print("Data shape before thresholding: ", np.shape(X_new))
X_new_th = X_new
if ui.rbMat.isChecked() and ui.cbThreshold.isChecked():
index = np.where(np.std(X_new, axis=0) >= Threshold)
X_new_index = X_new[:, index]
print("Standard deviation thresholding ...")
X_new_th = None
for xx_new in X_new_index:
X_new_th = xx_new if X_new_th is None else np.concatenate(
(X_new_th, xx_new), axis=0)
# Apply Normalization
if ui.rbMat.isChecked() and ui.cbOutNormal.isChecked():
X_new_th = X_new_th - np.mean(X_new_th)
X_new_th = X_new_th / np.std(X_new_th)
print("Output data is normalized!")
OutData[ui.txtOData.text()] = X_new_th
print("Saving ...")
print("Final data shape: ", np.shape(OutData[ui.txtOData.text()]))
mainIO_save(OutData, ui.txtOutFile.text())
print("DONE.")
msgBox.setText("CNN is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnConvert_click(self):
msgBox = QMessageBox()
tStart = time.time()
if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
msgBox.setText("At least, you must select one metric!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Method
method = ui.cbMethod.currentData()
# Solver
solver = ui.cbSolver.currentText()
# Selection
selection = ui.cbSelection.currentText()
# Fit
fit = ui.cbFit.isChecked()
# normalize
normalize = ui.cbNormalize.isChecked()
try:
alpha = np.float(ui.txtAlpha.text())
except:
msgBox.setText("Alpha is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
iter = np.int(ui.txtMaxIter.text())
except:
msgBox.setText("Max Iteration is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
tol = np.float(ui.txtTole.text())
except:
msgBox.setText("Tolerance is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
l1 = np.float(ui.txtL1.text())
except:
msgBox.setText("L1 is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
njob = np.float(ui.txtJobs.text())
except:
msgBox.setText("Number of jobs is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Filter
try:
Filter = ui.txtFilter.text()
if not len(Filter):
Filter = None
else:
Filter = Filter.replace("\'", " ").replace(",", " ").replace(
"[", "").replace("]", "").split()
Filter = np.int32(Filter)
except:
print("Filter is wrong!")
return
# OutFile
OutFile = ui.txtOutFile.text()
if not len(OutFile):
msgBox.setText("Please enter out file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
OutData = dict()
# InFile
InFile = ui.txtInFile.text()
if not len(InFile):
msgBox.setText("Please enter input file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not os.path.isfile(InFile):
msgBox.setText("Input file not found!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Loading ...")
InData = mainIO_load(InFile)
# Data
if not len(ui.txtData.currentText()):
msgBox.setText("Please enter Input Data variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Label
if not len(ui.txtLabel.currentText()):
msgBox.setText("Please enter Train Label variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Design
if not len(ui.txtDesign.currentText()):
msgBox.setText("Please enter Input Design variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Design = InData[ui.txtDesign.currentText()]
except:
msgBox.setText("Design value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Condition
if not len(ui.txtCond.currentText()):
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Cond = InData[ui.txtCond.currentText()]
OutData[ui.txtCond.currentText()] = Cond
labels = list()
for con in Cond:
labels.append(reshape_condition_cell(con[1]))
except:
msgBox.setText("Condition value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
FontSize = ui.txtFontSize.value()
try:
X = InData[ui.txtData.currentText()]
L = InData[ui.txtLabel.currentText()][0]
if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
X = preprocessing.scale(X)
print("Whole of data is scaled X~N(0,1).")
except:
print("Cannot load data or label")
return
# Task
if not len(ui.txtTask.currentText()):
msgBox.setText("Please enter Task variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
TaskTitle = np.array(InData[ui.txtTask.currentText()][0])
except:
msgBox.setText("Task variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
TaskTitleUnique = np.unique(TaskTitle)
Task = np.zeros(np.shape(TaskTitle))
for ttinx, tt in enumerate(TaskTitle):
for ttlinx, ttl in enumerate(TaskTitleUnique):
if tt[0] == ttl:
Task[ttinx] = ttlinx + 1
break
# Subject
if not len(ui.txtSubject.currentText()):
msgBox.setText("Please enter Subject variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Sub = InData[ui.txtSubject.currentText()][0]
except:
msgBox.setText("Subject variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Run
if not len(ui.txtRun.currentText()):
msgBox.setText("Please enter Run variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Run = InData[ui.txtRun.currentText()][0]
except:
msgBox.setText("Run variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
# Counter
if not len(ui.txtCounter.currentText()):
msgBox.setText("Please enter Counter variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
try:
Con = InData[ui.txtCounter.currentText()][0]
except:
msgBox.setText("Counter variable name is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Filter is not None:
for fil in Filter:
# Remove Training Set
labelIndx = np.where(L == fil)[0]
Design = np.delete(Design, labelIndx, axis=0)
X = np.delete(X, labelIndx, axis=0)
L = np.delete(L, labelIndx, axis=0)
Task = np.delete(Task, labelIndx, axis=0)
Sub = np.delete(Sub, labelIndx, axis=0)
Run = np.delete(Run, labelIndx, axis=0)
Con = np.delete(Con, labelIndx, axis=0)
print("Class ID = " + str(fil) + " is removed from data.")
try:
Unit = np.int32(ui.txtUnit.text())
except:
msgBox.setText("Unit for the test set must be a number!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if Unit < 1:
msgBox.setText("Unit for the test set must be greater than zero!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
print("Calculating Levels ...")
GroupFold = None
FoldStr = ""
if ui.cbFSubject.isChecked():
if not ui.rbFRun.isChecked():
GroupFold = [Sub]
FoldStr = "Subject"
else:
GroupFold = np.concatenate(([Sub], [Run]))
FoldStr = "Subject+Run"
if ui.cbFTask.isChecked():
GroupFold = np.concatenate(
(GroupFold, [Task])) if GroupFold is not None else [Task]
FoldStr = FoldStr + "+Task"
if ui.cbFCounter.isChecked():
GroupFold = np.concatenate(
(GroupFold, [Con])) if GroupFold is not None else [Con]
FoldStr = FoldStr + "+Counter"
if FoldStr == "":
FoldStr = "Whole-Data"
GUFold = [1]
ListFold = [1]
UniqFold = [1]
GroupFold = [1]
UnitFold = np.ones((1, np.shape(X)[0]))
else:
GroupFold = np.transpose(GroupFold)
UniqFold = np.array(list(set(tuple(i)
for i in GroupFold.tolist())))
FoldIDs = np.arange(len(UniqFold)) + 1
if len(UniqFold) <= Unit:
msgBox.setText(
"Unit must be smaller than all possible levels! Number of all levels is: "
+ str(len(UniqFold)))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if np.mod(len(UniqFold), Unit):
msgBox.setText(
"Unit must be divorceable to all possible levels! Number of all levels is: "
+ str(len(UniqFold)))
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
ListFold = list()
for gfold in GroupFold:
for ufoldindx, ufold in enumerate(UniqFold):
if (ufold == gfold).all():
currentID = FoldIDs[ufoldindx]
break
ListFold.append(currentID)
ListFold = np.int32(ListFold)
if Unit == 1:
UnitFold = np.int32(ListFold)
else:
UnitFold = np.int32((ListFold - 0.1) / Unit) + 1
GUFold = np.unique(UnitFold)
FoldInfo = dict()
FoldInfo["Unit"] = Unit
FoldInfo["Group"] = GroupFold
FoldInfo["Order"] = FoldStr
FoldInfo["List"] = ListFold
FoldInfo["Unique"] = UniqFold
FoldInfo["Folds"] = UnitFold
OutData["FoldInfo"] = FoldInfo
OutData["ModelAnalysis"] = "SK.Group.RSA." + ui.cbMethod.currentText()
print("Number of all levels is: " + str(len(UniqFold)))
Cov = None
Corr = None
AMSE = list()
Beta = None
for foldID, fold in enumerate(GUFold):
print("Analyzing level " + str(foldID + 1), " of ",
str(len(UniqFold)), " ...")
Index = np.where(UnitFold == fold)
# Whole-Data
if FoldStr == "Whole-Data" and np.shape(Index)[0]:
Index = [Index[1]]
XLi = X[Index]
if ui.cbScale.isChecked() and ui.rbScale.isChecked():
XLi = preprocessing.scale(XLi)
print("Whole of data is scaled X%d~N(0,1)." % (foldID + 1))
RegLi = np.insert(Design[Index], 0, 1, axis=1)
if method == "ols":
model = linmdl.LinearRegression(fit_intercept=fit,
normalize=normalize,
n_jobs=njob)
elif method == "ridge":
model = linmdl.Ridge(alpha=alpha,
fit_intercept=fit,
normalize=normalize,
max_iter=iter,
tol=tol,
solver=solver)
elif method == "lasso":
model = linmdl.Lasso(alpha=alpha,
fit_intercept=fit,
normalize=normalize,
max_iter=iter,
tol=tol,
selection=selection)
elif method == "elast":
model = linmdl.ElasticNet(alpha=alpha, l1_ratio=l1, fit_intercept=fit, normalize=normalize, \
max_iter=iter, tol=tol, selection=selection)
model.fit(RegLi, XLi)
BetaLi = np.transpose(model.coef_)[1:, :]
Beta = BetaLi if Beta is None else Beta + BetaLi
print("Calculating MSE for level %d ..." % (foldID + 1))
MSE = mean_squared_error(XLi, np.matmul(Design[Index], BetaLi))
print("MSE%d: %f" % (foldID + 1, MSE))
OutData["MSE" + str(foldID)] = MSE
AMSE.append(MSE)
if ui.cbBeta.isChecked():
OutData["BetaL" + str(foldID + 1)] = BetaLi
# Calculate Results
if ui.cbCorr.isChecked():
print("Calculating Correlation for level %d ..." %
(foldID + 1))
CorrLi = np.corrcoef(BetaLi)
OutData["Corr" + str(foldID + 1)] = CorrLi
if Corr is None:
Corr = CorrLi.copy()
else:
if ui.rbAvg.isChecked():
Corr = np.add(Corr, CorrLi)
elif ui.rbMin.isChecked():
Corr = np.minimum(Corr, CorrLi)
else:
Corr = np.maximum(Corr, CorrLi)
if ui.cbCov.isChecked():
print("Calculating Covariance for level %d ..." % (foldID + 1))
CovLi = np.cov(BetaLi)
OutData["Cov" + str(foldID + 1)] = CovLi
if Cov is None:
Cov = CovLi.copy()
else:
if ui.rbAvg.isChecked():
Cov = np.add(Cov, CovLi)
elif ui.rbMin.isChecked():
Cov = np.minimum(Cov, CovLi)
else:
Cov = np.maximum(Cov, CovLi)
CoEff = len(UniqFold) - 1 if len(UniqFold) > 2 else 1
if ui.cbCov.isChecked():
if ui.rbAvg.isChecked():
Cov = Cov / CoEff
covClass = SimilarityMatrixBetweenClass(Cov)
OutData["Covariance"] = Cov
OutData["Covariance_min"] = covClass.min()
OutData["Covariance_max"] = covClass.max()
OutData["Covariance_std"] = covClass.std()
OutData["Covariance_mean"] = covClass.mean()
if ui.cbCorr.isChecked():
if ui.rbAvg.isChecked():
Corr = Corr / CoEff
corClass = SimilarityMatrixBetweenClass(Corr)
OutData["Correlation"] = Corr
OutData["Correlation_min"] = corClass.min()
OutData["Correlation_max"] = corClass.max()
OutData["Correlation_std"] = corClass.std()
OutData["Correlation_mean"] = corClass.mean()
# Calculating Distance Matrix
dis = np.zeros((np.shape(Beta)[0], np.shape(Beta)[0]))
for i in range(np.shape(Beta)[0]):
for j in range(i + 1, np.shape(Beta)[0]):
dis[i, j] = 1 - np.dot(Beta[i, :], Beta[j, :].T)
dis[j, i] = dis[i, j]
OutData["DistanceMatrix"] = dis
Z = linkage(dis)
OutData["Linkage"] = Z
OutData["MSE"] = np.mean(AMSE)
print("Average MSE: %f" % (OutData["MSE"]))
OutData["RunTime"] = time.time() - tStart
print("Runtime (s): %f" % (OutData["RunTime"]))
print("Saving results ...")
mainIO_save(OutData, OutFile)
print("Output is saved.")
if ui.cbDiagram.isChecked():
if ui.cbCorr.isChecked():
NumData = np.shape(Corr)[0]
fig1 = plt.figure(num=None,
figsize=(NumData, NumData),
dpi=100)
plt.pcolor(Corr, vmin=np.min(Corr), vmax=np.max(Corr))
plt.xlim([0, NumData])
plt.ylim([0, NumData])
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=FontSize)
ax = plt.gca()
ax.invert_yaxis()
ax.set_aspect(1)
ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticklabels(labels,
minor=False,
fontsize=FontSize,
rotation=ui.txtXRotation.value())
ax.set_yticklabels(labels,
minor=False,
fontsize=FontSize,
rotation=ui.txtYRotation.value())
ax.grid(False)
ax.set_aspect(1)
ax.set_frame_on(False)
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
if len(ui.txtTitleCorr.text()):
plt.title(ui.txtTitleCorr.text())
else:
plt.title('Group RSA: Correlation\nLevel: ' + FoldStr)
plt.show()
if ui.cbCov.isChecked():
NumData = np.shape(Cov)[0]
fig2 = plt.figure(num=None,
figsize=(NumData, NumData),
dpi=100)
plt.pcolor(Cov, vmin=np.min(Cov), vmax=np.max(Cov))
plt.xlim([0, NumData])
plt.ylim([0, NumData])
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=FontSize)
ax = plt.gca()
ax.invert_yaxis()
ax.set_aspect(1)
ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticklabels(labels,
minor=False,
fontsize=FontSize,
rotation=ui.txtXRotation.value())
ax.set_yticklabels(labels,
minor=False,
fontsize=FontSize,
rotation=ui.txtYRotation.value())
ax.grid(False)
ax.set_aspect(1)
ax.set_frame_on(False)
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
if len(ui.txtTitleCov.text()):
plt.title(ui.txtTitleCov.text())
else:
plt.title('Group RSA: Covariance\nLevel: ' + FoldStr)
plt.show()
fig3 = plt.figure(figsize=(25, 10), )
if len(ui.txtTitleDen.text()):
plt.title(ui.txtTitleDen.text())
else:
plt.title(
'Group MP Gradient RSA: Similarity Analysis\nLevel: ' +
FoldStr)
dn = dendrogram(Z,
labels=labels,
leaf_font_size=FontSize,
color_threshold=1,
leaf_rotation=ui.txtXRotation.value())
plt.show()
print("DONE.")
msgBox.setText(
"Group Representational Similarity Analysis (RSA) is done.")
msgBox.setIcon(QMessageBox.Information)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
def btnInFile_click(self):
filename = LoadFile("Load data file ...",['Data files (*.ezx *.mat *.ezdata)'],'ezx',\
os.path.dirname(ui.txtInFile.text()))
if len(filename):
if os.path.isfile(filename):
try:
data = mainIO_load(filename)
Keys = data.keys()
# Data
ui.txtData.clear()
HasDefualt = False
for key in Keys:
ui.txtData.addItem(key)
if key == "data":
HasDefualt = True
if HasDefualt:
ui.txtData.setCurrentText("data")
# Label
ui.txtLabel.clear()
HasDefualt = False
for key in Keys:
ui.txtLabel.addItem(key)
if key == "label":
HasDefualt = True
if HasDefualt:
ui.txtLabel.setCurrentText("label")
# mLabel
ui.txtmLabel.clear()
HasDefualt = False
for key in Keys:
ui.txtmLabel.addItem(key)
if key == "mlabel":
HasDefualt = True
if HasDefualt:
ui.txtmLabel.setCurrentText("mlabel")
ui.cbmLabel.setChecked(HasDefualt)
# Coordinate
ui.txtCol.clear()
HasDefualt = False
for key in Keys:
ui.txtCol.addItem(key)
if key == "coordinate":
HasDefualt = True
if HasDefualt:
ui.txtCol.setCurrentText("coordinate")
ui.cbCol.setChecked(HasDefualt)
# Design
ui.txtDM.clear()
HasDefualt = False
for key in Keys:
ui.txtDM.addItem(key)
if key == "design":
HasDefualt = True
if HasDefualt:
ui.txtDM.setCurrentText("design")
ui.cbDM.setChecked(HasDefualt)
# Subject
ui.txtSubject.clear()
HasDefualt = False
for key in Keys:
ui.txtSubject.addItem(key)
if key == "subject":
HasDefualt = True
if HasDefualt:
ui.txtSubject.setCurrentText("subject")
# Task
ui.txtTask.clear()
HasDefualt = False
for key in Keys:
ui.txtTask.addItem(key)
if key == "task":
HasDefualt = True
if HasDefualt:
ui.txtTask.setCurrentText("task")
ui.cbTask.setChecked(HasDefualt)
# Run
ui.txtRun.clear()
HasDefualt = False
for key in Keys:
ui.txtRun.addItem(key)
if key == "run":
HasDefualt = True
if HasDefualt:
ui.txtRun.setCurrentText("run")
ui.cbRun.setChecked(HasDefualt)
# Counter
ui.txtCounter.clear()
HasDefualt = False
for key in Keys:
ui.txtCounter.addItem(key)
if key == "counter":
HasDefualt = True
if HasDefualt:
ui.txtCounter.setCurrentText("counter")
ui.cbCounter.setChecked(HasDefualt)
# Condition
ui.txtCond.clear()
HasDefualt = False
for key in Keys:
ui.txtCond.addItem(key)
if key == "condition":
HasDefualt = True
if HasDefualt:
ui.txtCond.setCurrentText("condition")
ui.cbCond.setChecked(HasDefualt)
# NScan
ui.txtScan.clear()
HasDefualt = False
for key in Keys:
ui.txtScan.addItem(key)
if key == "nscan":
HasDefualt = True
if HasDefualt:
ui.txtScan.setCurrentText("nscan")
ui.cbNScan.setChecked(HasDefualt)
ui.txtInFile.setText(filename)
except Exception as e:
print(e)
print("Cannot load data file!")
return
else:
print("File not found!")
def btnRedraw_click(self):
msgBox = QMessageBox()
ofile = LoadFile("Save result file ...", ['Result files (*.ezx *.mat)'], 'ezx',\
os.path.dirname(ui.txtOutFile.text()))
FontSize = ui.txtFontSize.value()
if len(ofile):
try:
Res = mainIO_load(ofile)
except:
print("Cannot load result file!")
msgBox.setText("Cannot load result file!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
HasDefaultCond = False
# Condition
if not len(ui.txtCond.currentText()):
try:
Cond = Res["condition"]
HasDefaultCond = True
except:
msgBox.setText("Please enter Condition variable name!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
if not HasDefaultCond:
try:
Cond = Res[ui.txtCond.currentText()]
except:
msgBox.setText("Condition value is wrong!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
labels = list()
for con in Cond:
labels.append(reshape_condition_cell(con[1]))
if ui.cbCorr.isChecked():
try:
Corr = Res["Correlation"]
except:
print("Cannot load Correlation variable!")
msgBox.setText("Cannot load Correlation variable!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
NumData = np.shape(Corr)[0]
fig1 = plt.figure(num=None,
figsize=(NumData, NumData),
dpi=100)
plt.pcolor(Corr, vmin=np.min(Corr), vmax=np.max(Corr))
plt.xlim([0, NumData])
plt.ylim([0, NumData])
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=FontSize)
ax = plt.gca()
ax.invert_yaxis()
ax.set_aspect(1)
ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticklabels(labels,
minor=False,
fontsize=FontSize,
rotation=ui.txtXRotation.value())
ax.set_yticklabels(labels,
minor=False,
fontsize=FontSize,
rotation=ui.txtYRotation.value())
ax.grid(False)
ax.set_aspect(1)
ax.set_frame_on(False)
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
if len(ui.txtTitleCov.text()):
plt.title(ui.txtTitleCov.text())
else:
plt.title('Group RSA: Correlation\nLevel: ' +
str(Res["FoldInfo"]["Order"][0][0][0]))
plt.show()
if ui.cbCov.isChecked():
try:
Cov = Res["Covariance"]
except:
print("Cannot load Covariance variable!")
msgBox.setText("Cannot load Covariance variable!")
msgBox.setIcon(QMessageBox.Critical)
msgBox.setStandardButtons(QMessageBox.Ok)
msgBox.exec_()
return False
NumData = np.shape(Cov)[0]
fig2 = plt.figure(num=None,
figsize=(NumData, NumData),
dpi=100)
plt.pcolor(Cov, vmin=np.min(Cov), vmax=np.max(Cov))
plt.xlim([0, NumData])
plt.ylim([0, NumData])
cbar = plt.colorbar()
cbar.ax.tick_params(labelsize=FontSize)
ax = plt.gca()
ax.invert_yaxis()
ax.set_aspect(1)
ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
ax.set_xticklabels(labels,
minor=False,
fontsize=FontSize,
rotation=ui.txtXRotation.value())
ax.set_yticklabels(labels,
minor=False,
fontsize=FontSize,
rotation=ui.txtYRotation.value())
ax.grid(False)
ax.set_aspect(1)
ax.set_frame_on(False)
for t in ax.xaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
for t in ax.yaxis.get_major_ticks():
t.tick1On = False
t.tick2On = False
if len(ui.txtTitleCorr.text()):
plt.title(ui.txtTitleCorr.text())
else:
plt.title('Group RSA: Covariance\nLevel: ' +
str(Res["FoldInfo"]["Order"][0][0][0]))
plt.show()
fig3 = plt.figure(figsize=(25, 10), )
dn = dendrogram(Res["Linkage"],
labels=labels,
leaf_font_size=FontSize,
color_threshold=1,
leaf_rotation=ui.txtXRotation.value())
if len(ui.txtTitleDen.text()):
plt.title(ui.txtTitleDen.text())
else:
plt.title(
'Group MP Gradient RSA: Similarity Analysis\nLevel: ' +
str(Res["FoldInfo"]["Order"][0][0][0]))
plt.show()
