#/*########################################################################## # Copyright (C) 2004-2022 European Synchrotron Radiation Facility # # This file is part of the PyMca X-ray Fluorescence Toolkit developed at # the ESRF. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. # #############################################################################*/ __author__ = "V.A. Sole - ESRF" __contact__ = "sole@esrf.fr" __license__ = "MIT" __copyright__ = "European Synchrotron Radiation Facility, Grenoble, France" __doc__ = """ Fast XRF Linear fit of data stack by: - fixing non-linear parameters to its starting values - processing the data in chunks instead of point by point """ import sys import os import numpy import logging import traceback from PyMca5 import StackPluginBase from PyMca5.PyMcaPhysics import FastXRFLinearFit from PyMca5.PyMcaPhysics import XRFBatchFitOutput from PyMca5.PyMcaGui import FastXRFLinearFitWindow from PyMca5.PyMcaGui import CalculationThread from PyMca5.PyMcaGui import StackPluginResultsWindow from PyMca5.PyMcaGui import PyMca_Icons as PyMca_Icons from PyMca5.PyMcaGui import PyMcaQt as qt _logger = logging.getLogger(__name__) class FastXRFLinearFitStackPlugin(StackPluginBase.StackPluginBase): """ Fast XRF Linear fit of data stack by: - fixing non-linear parameters to its starting values - processing the data in chunks instead of point by point """ def __init__(self, stackWindow, **kw): if _logger.getEffectiveLevel() == logging.DEBUG: StackPluginBase.pluginBaseLogger.setLevel(logging.DEBUG) StackPluginBase.StackPluginBase.__init__(self, stackWindow, **kw) self.methodDict = {} function = self.calculate info = "Fast XRF Linear fit of data stack" icon = PyMca_Icons.fit self.methodDict["Fit Stack"] =[function, info, icon] function = self._showWidget info = "Show last results" icon = PyMca_Icons.brushselect self.methodDict["Show"] =[function, info, icon] self.__methodKeys = ["Fit Stack", "Show"] self.configurationWidget = None self.fitInstance = None self._widget = None self.thread = None def stackUpdated(self): _logger.debug("FastXRFLinearFitStackPlugin.stackUpdated() called") self._widget = None def selectionMaskUpdated(self): if self._widget is None: return if self._widget.isHidden(): return mask = self.getStackSelectionMask() self._widget.setSelectionMask(mask) def mySlot(self, ddict): _logger.debug("mySlot ", ddict['event'], ddict.keys()) if ddict['event'] == "selectionMaskChanged": self.setStackSelectionMask(ddict['current']) elif ddict['event'] == "addImageClicked": self.addImage(ddict['image'], ddict['title']) elif ddict['event'] == "addAllClicked": for i in range(len(ddict["images"])): self.addImage(ddict['images'][i], ddict['titles'][i]) elif ddict['event'] == "removeImageClicked": self.removeImage(ddict['title']) elif ddict['event'] == "replaceImageClicked": self.replaceImage(ddict['image'], ddict['title']) elif ddict['event'] == "resetSelection": self.setStackSelectionMask(None) #Methods implemented by the plugin def getMethods(self): if self._widget is None: return [self.__methodKeys[0]] else: return self.__methodKeys def getMethodToolTip(self, name): return self.methodDict[name][1] def getMethodPixmap(self, name): return self.methodDict[name][2] def applyMethod(self, name): return self.methodDict[name][0]() # The specific part def calculate(self): if self.configurationWidget is None: self.configurationWidget = \ FastXRFLinearFitWindow.FastXRFLinearFitDialog() ret = self.configurationWidget.exec() if ret: self._executeFunctionAndParameters() def _executeFunctionAndParameters(self): self._parameters = self.configurationWidget.getParameters() self._widget = None if self.fitInstance is None: self.fitInstance = FastXRFLinearFit.FastXRFLinearFit() #self._fitConfigurationFile="E:\DATA\COTTE\CH1777\G4-4720eV-NOWEIGHT-Constant-batch.cfg" if _logger.getEffectiveLevel() == logging.DEBUG: self.thread = CalculationThread.CalculationThread(\ calculation_method=self.actualCalculation) self.thread.result = self.actualCalculation() self.threadFinished() else: self.thread = CalculationThread.CalculationThread(\ calculation_method=self.actualCalculation) self.thread.finished.connect(self.threadFinished) self.thread.start() message = "Please wait. Calculation going on." CalculationThread.waitingMessageDialog(self.thread, parent=self.configurationWidget, message=message) def actualCalculation(self): activeCurve = self.getActiveCurve() if activeCurve is not None: x, spectrum, legend, info = activeCurve else: x = None spectrum = None if not self.isStackFinite(): # one has to check for NaNs in the used region(s) # for the time being only in the global image # spatial_mask = numpy.isfinite(image_data) spatial_mask = numpy.isfinite(self.getStackOriginalImage()) # WDN: any effect? stack = self.getStackDataObject() fitparams = self._parameters['fit'].copy() fitConfigurationFile = fitparams.pop('configuration') self.fitInstance.setFitConfigurationFile(fitConfigurationFile) if fitparams['weight']: # force calculation of the unnormalized sum spectrum spectrum = None if stack.x in [None, []]: x = None else: x = stack.x[0] outparams = self._parameters['output'] outbuffer = XRFBatchFitOutput.OutputBuffer(**outparams) outbuffer = self.fitInstance.fitMultipleSpectra(x=x, y=stack, ysum=spectrum, outbuffer=outbuffer, save=False, # do it later **fitparams) return outbuffer def threadFinished(self): try: self._threadFinished() except: msg = qt.QMessageBox() msg.setIcon(qt.QMessageBox.Critical) msg.setInformativeText(str(sys.exc_info()[1])) msg.setDetailedText(traceback.format_exc()) msg.exec() def _threadFinished(self): result = self.thread.result self.thread = None if type(result) == type((1,)): #if we receive a tuple there was an error if len(result): if result[0] == "Exception": # somehow this exception is not caught raise Exception(result[1], result[2])#, result[3]) return # Show results with result.bufferContext(update=True): if 'molarconcentrations' in result: imageNames = result.labels('parameters', labeltype='title') + \ result.labels('molarconcentrations', labeltype='title') images = numpy.concatenate((result['parameters'], result['molarconcentrations']), axis=0) elif 'massfractions' in result: imageNames = result.labels('parameters', labeltype='title') + \ result.labels('massfractions', labeltype='title') images = numpy.concatenate((result['parameters'], result['massfractions']), axis=0) else: imageNames = result.labels('parameters', labeltype='title') images = result['parameters'] self._widget = StackPluginResultsWindow.StackPluginResultsWindow(\ usetab=False) self._widget.buildAndConnectImageButtonBox(replace=True, multiple=True) qt = StackPluginResultsWindow.qt self._widget.sigMaskImageWidgetSignal.connect(self.mySlot) self._widget.setStackPluginResults(images, image_names=imageNames) self._showWidget() # Save results result.save() def _showWidget(self): if self._widget is None: return #Show self._widget.show() self._widget.raise_() #update self.selectionMaskUpdated() MENU_TEXT = "Fast XRF Linear Fit" def getStackPluginInstance(stackWindow, **kw): ob = FastXRFLinearFitStackPlugin(stackWindow) return ob