def init(config):
global advanced_fit, mass_fraction_tool
from PyMca5.PyMcaPhysics.xrf.ClassMcaTheory import McaTheory
config['fit']['use_limit'] = 1
advanced_fit = McaTheory(config=config)
advanced_fit.enableOptimizedLinearFit()
if 'concentrations' in config:
mass_fraction_tool = ConcentrationsTool(config['concentrations'])
mass_fraction_tool.config['time'] = 1
else:
mass_fraction_tool = None
def init(config):
global advanced_fit, mass_fraction_tool
from PyMca5.PyMcaPhysics.xrf.ClassMcaTheory import McaTheory
config['fit']['use_limit'] = 1
advanced_fit = McaTheory(config=config)
advanced_fit.enableOptimizedLinearFit()
if 'concentrations' in config:
mass_fraction_tool = ConcentrationsTool(config['concentrations'])
mass_fraction_tool.config['time'] = 1
else:
mass_fraction_tool = None
def __init__(self, concentrationsWidget=None, parent=None):
QtGui.QWidget.__init__(self, parent)
uic.loadUi(resources['mcaspectrum.ui'], self)
self.concentrationsWidget = concentrationsWidget
self.mcafit = McaTheory()
# self.mcafit.enableOptimizedLinearFit()
self.fitData = None
self.figure = McaSpectrumFigure(self)
self.toolbar = MplToolbar(self.figure, self)
self.gridlayout1.addWidget(self.toolbar, 0, 0, 1, 1)
self.gridlayout1.addWidget(self.figure, 1, 0, 1, 1)
self.mcaLogscaleButton.clicked.connect(self.enableLogscale)
def __init__(self, parent=None):
super(SpectrumAnalysisThread, self).__init__(parent)
self.mcafit = McaTheory()
self.timer = QtCore.QTimer(self)
self.timer.timeout.connect(self.update)
self.timer.start(20)
def __init__(self, concentrationsWidget=None, parent=None):
QtGui.QWidget.__init__(self, parent)
uic.loadUi(resources['mcaspectrum.ui'], self)
self.concentrationsWidget = concentrationsWidget
self.mcafit = McaTheory()
# self.mcafit.enableOptimizedLinearFit()
self.fitData = None
self.figure = McaSpectrumFigure(self)
self.toolbar = MplToolbar(self.figure, self)
self.gridlayout1.addWidget(self.toolbar, 0, 0, 1, 1)
self.gridlayout1.addWidget(self.figure, 1, 0, 1, 1)
self.mcaLogscaleButton.clicked.connect(self.enableLogscale)
class McaSpectrum(QtGui.QWidget):
"""
"""
def __init__(self, concentrationsWidget=None, parent=None):
QtGui.QWidget.__init__(self, parent)
uic.loadUi(resources['mcaspectrum.ui'], self)
self.concentrationsWidget = concentrationsWidget
self.mcafit = McaTheory()
# self.mcafit.enableOptimizedLinearFit()
self.fitData = None
self.figure = McaSpectrumFigure(self)
self.toolbar = MplToolbar(self.figure, self)
self.gridlayout1.addWidget(self.toolbar, 0, 0, 1, 1)
self.gridlayout1.addWidget(self.figure, 1, 0, 1, 1)
self.mcaLogscaleButton.clicked.connect(self.enableLogscale)
def __getattr__(self, attr):
return getattr(self.figure, attr)
def configure(self, configDict):
newDict = self.mcafit.configure(configDict)
try:
self.concentrationsWidget.setParameters(newDict['concentrations'],
signal=False)
except KeyError:
pass
return newDict
def enableInteraction(self):
self.mcaAutoscaleButton.setEnabled(True)
self.mcaLogscaleButton.setEnabled(True)
def enableLogscale(self, val):
self.figure.useLogScale = val
self.updateFigure()
def fit(self):
if self.mcafit.config['peaks'] == {}:
msg = QtGui.QMessageBox(self)
msg.setIcon(QtGui.QMessageBox.Information)
msg.setText("No peaks defined.\nPlease configure peaks")
msg.exec_()
return
self.mcafit.estimate()
fitresult, fitData = self.mcafit.startfit(digest=1)
self.fitData = copy.deepcopy(fitData)
self.peaksSpectrum()
self.updateFigure()
config = self.mcafit.config
if 'concentrations' in self.mcafit.config:
fitresult = {'fitresult': fitresult, 'result': self.fitData}
tool = self.concentrationsWidget
toolconfig = tool.getParameters()
concDict = config['concentrations']
tool.setParameters(concDict, signal=False)
try:
dict = tool.processFitResult(config=concDict,
fitresult=fitresult,
elementsfrommatrix=False,
fluorates=self.mcafit._fluoRates)
except:
msg = qt.QMessageBox(self)
msg.setIcon(qt.QMessageBox.Critical)
msg.setText("Error processing fit result: %s" %
(sys.exc_info()[1]))
msg.exec_()
return copy.deepcopy(fitresult)
def peaksSpectrum(self):
fitresult = self.fitData
config = self.mcafit.configure()
groupsList = fitresult['groups']
if not isinstance(groupsList, list): groupsList = [groupsList]
nglobal = len(fitresult['parameters']) - len(groupsList)
dict = self.fitData
newparameters = fitresult['fittedpar'] * 1
for i in range(nglobal, len(fitresult['parameters'])):
newparameters[i] = 0.0
for i in range(nglobal, len(fitresult['parameters'])):
group = fitresult['parameters'][i]
group = fitresult['parameters'][i]
parameters = newparameters * 1
parameters[i] = fitresult['fittedpar'][i]
xmatrix = fitresult['xdata']
ymatrix = self.mcafit.mcatheory(parameters, xmatrix)
ymatrix.shape = [len(ymatrix), 1]
label = 'y' + group
if self.mcafit.STRIP:
dict[label] = ymatrix + self.mcafit.zz
else:
dict[label] = ymatrix
dict[label].shape = (len(dict[label]), )
self.fitData[label] = dict[label] * 1.0
def setData(self, *args, **kwargs):
self.mcafit.setData(*args, **kwargs)
def updateFigure(self):
self.figure.updateFigure(self.fitData)
class McaSpectrum(QtGui.QWidget):
"""
"""
def __init__(self, concentrationsWidget=None, parent=None):
QtGui.QWidget.__init__(self, parent)
uic.loadUi(resources['mcaspectrum.ui'], self)
self.concentrationsWidget = concentrationsWidget
self.mcafit = McaTheory()
# self.mcafit.enableOptimizedLinearFit()
self.fitData = None
self.figure = McaSpectrumFigure(self)
self.toolbar = MplToolbar(self.figure, self)
self.gridlayout1.addWidget(self.toolbar, 0, 0, 1, 1)
self.gridlayout1.addWidget(self.figure, 1, 0, 1, 1)
self.mcaLogscaleButton.clicked.connect(self.enableLogscale)
def __getattr__(self, attr):
return getattr(self.figure, attr)
def configure(self, configDict):
newDict = self.mcafit.configure(configDict)
try:
self.concentrationsWidget.setParameters(newDict['concentrations'],
signal=False)
except KeyError:
pass
return newDict
def enableInteraction(self):
self.mcaAutoscaleButton.setEnabled(True)
self.mcaLogscaleButton.setEnabled(True)
def enableLogscale(self, val):
self.figure.useLogScale = val
self.updateFigure()
def fit(self):
if self.mcafit.config['peaks'] == {}:
msg = QtGui.QMessageBox(self)
msg.setIcon(QtGui.QMessageBox.Information)
msg.setText("No peaks defined.\nPlease configure peaks")
msg.exec_()
return
self.mcafit.estimate()
fitresult, fitData = self.mcafit.startfit(digest=1)
self.fitData = copy.deepcopy(fitData)
self.peaksSpectrum()
self.updateFigure()
config = self.mcafit.config
if 'concentrations' in self.mcafit.config:
fitresult = {'fitresult': fitresult,
'result': self.fitData}
tool = self.concentrationsWidget
toolconfig = tool.getParameters()
concDict = config['concentrations']
tool.setParameters(concDict, signal=False)
try:
dict = tool.processFitResult(config=concDict,
fitresult=fitresult,
elementsfrommatrix=False,
fluorates=self.mcafit._fluoRates)
except:
msg = qt.QMessageBox(self)
msg.setIcon(qt.QMessageBox.Critical)
msg.setText("Error processing fit result: %s" % (sys.exc_info()[1]))
msg.exec_()
return copy.deepcopy(fitresult)
def peaksSpectrum(self):
fitresult = self.fitData
config = self.mcafit.configure()
groupsList = fitresult['groups']
if not isinstance(groupsList, list): groupsList = [groupsList]
nglobal = len(fitresult['parameters']) - len(groupsList)
dict = self.fitData
newparameters = fitresult['fittedpar'] * 1
for i in range(nglobal, len(fitresult['parameters'])):
newparameters[i] = 0.0
for i in range(nglobal, len(fitresult['parameters'])):
group = fitresult['parameters'][i]
group = fitresult['parameters'][i]
parameters = newparameters * 1
parameters[i] = fitresult['fittedpar'][i]
xmatrix = fitresult['xdata']
ymatrix = self.mcafit.mcatheory(parameters, xmatrix)
ymatrix.shape = [len(ymatrix), 1]
label = 'y'+group
if self.mcafit.STRIP:
dict[label] = ymatrix + self.mcafit.zz
else:
dict[label] = ymatrix
dict[label].shape = (len(dict[label]),)
self.fitData[label] = dict[label] * 1.0
def setData(self, *args, **kwargs):
self.mcafit.setData(*args, **kwargs)
def updateFigure(self):
self.figure.updateFigure(self.fitData)